Removed confusing ONNX RT EP deprecation note (#2400)

* Added Conda CentOS documentation 2020.4

* Added OS

.ci/azure/linux.yml

@@ -1,118 +0,0 @@
-jobs:
-- job: Lin
-  # About 150% of total time
-  timeoutInMinutes: 85
-  pool:
-    name: LIN_VMSS_VENV_F8S_WU2
-  variables:
-    system.debug: true
-    WORKERS_NUMBER: 8
-    BUILD_TYPE: Release
-    REPO_DIR: $(Build.Repository.LocalPath)
-    WORK_DIR: $(Pipeline.Workspace)/_w
-    BUILD_DIR: $(WORK_DIR)/build
-    BIN_DIR: $(REPO_DIR)/bin/intel64/$(BUILD_TYPE)
-  steps:
-  - checkout: self
-    clean: true
-    fetchDepth: 1
-    lfs: false
-    submodules: recursive
-    path: openvino
-  - script: |
-      curl -H Metadata:true --noproxy "*" "http://169.254.169.254/metadata/instance?api-version=2019-06-01"
-      whoami
-      uname -a
-      which python3
-      python3 --version
-      gcc --version
-      lsb_release
-      env
-      cat /proc/cpuinfo
-      cat /proc/meminfo
-      vmstat -s
-      df
-    displayName: 'System info'
-  - script: |
-      rm -rf $(WORK_DIR) ; mkdir $(WORK_DIR)
-      rm -rf $(BUILD_DIR) ; mkdir $(BUILD_DIR)
-    displayName: 'Make dir'
-  - script: |
-      sudo apt --assume-yes install libusb-1.0-0-dev
-      python3 -m pip install -r ./inference-engine/ie_bridges/python/requirements.txt
-      # For running Python API tests
-      python3 -m pip install -r ./inference-engine/ie_bridges/python/src/requirements-dev.txt
-    displayName: 'Install dependencies'
-  - script: |
-      wget https://github.com/ninja-build/ninja/releases/download/v1.10.0/ninja-linux.zip
-      unzip ninja-linux.zip
-      sudo cp -v ninja /usr/local/bin/
-    workingDirectory: $(WORK_DIR)
-    displayName: 'Install Ninja'
-  - task: CMake@1
-    inputs:
-      # CMake must get Python 3.x version by default
-      cmakeArgs: -GNinja -DVERBOSE_BUILD=ON -DCMAKE_BUILD_TYPE=$(BUILD_TYPE) -DENABLE_PYTHON=ON -DPYTHON_EXECUTABLE=/usr/bin/python3.6 -DENABLE_TESTS=ON $(REPO_DIR)
-      workingDirectory: $(BUILD_DIR)
-  - script: ninja
-    workingDirectory: $(BUILD_DIR)
-    displayName: 'Build Lin'
-  - script: ls -alR $(REPO_DIR)/bin/
-    displayName: 'List files'
-  - script: $(BIN_DIR)/unit-test --gtest_print_time=1 --gtest_filter=-backend_api.config_unsupported:*IE_GPU*
-    displayName: 'nGraph UT'
-    continueOnError: false
-  - script: $(BIN_DIR)/InferenceEngineUnitTests --gtest_print_time=1
-    displayName: 'IE UT old'
-    continueOnError: false
-  - script: $(BIN_DIR)/ieUnitTests
-    displayName: 'IE UT'
-    continueOnError: false
-  - script: $(BIN_DIR)/cpuUnitTests
-    displayName: 'CPU UT'
-    continueOnError: false
-  - script: $(BIN_DIR)/gnaUnitTests
-    displayName: 'GNA UT'
-    continueOnError: false
-  - script: $(BIN_DIR)/vpuUnitTests
-    displayName: 'VPU UT'
-    continueOnError: false
-  - script: $(BIN_DIR)/ieFuncTests
-    displayName: 'IE FuncTests'
-    continueOnError: false
-  - script: $(BIN_DIR)/cpuFuncTests --gtest_print_time=1
-    displayName: 'CPU FuncTests'
-    continueOnError: false
-  - script: $(BIN_DIR)/MklDnnBehaviorTests
-    displayName: 'MklDnnBehaviorTests'
-    continueOnError: false
-  - script: |
-      git clone https://github.com/openvinotoolkit/testdata.git
-      git clone https://github.com/google/gtest-parallel.git
-    workingDirectory: $(WORK_DIR)
-    displayName: 'Clone testdata & gtest-parallel'
-  - script: |
-      export DATA_PATH=$(WORK_DIR)/testdata
-      export MODELS_PATH=$(WORK_DIR)/testdata
-      python3 $(WORK_DIR)/gtest-parallel/gtest-parallel $(BIN_DIR)/MklDnnFunctionalTests --workers=$(WORKERS_NUMBER) --print_test_times --dump_json_test_results=MklDnnFunctionalTests.json -- --gtest_print_time=1
-    workingDirectory: $(WORK_DIR)
-    displayName: 'MklDnnFunctionalTests'
-    continueOnError: false
-  - script: |
-      export DATA_PATH=$(WORK_DIR)/testdata
-      export MODELS_PATH=$(WORK_DIR)/testdata
-      $(BIN_DIR)/InferenceEngineCAPITests
-    displayName: 'IE CAPITests'
-    continueOnError: false
-  - script: |
-      export DATA_PATH=$(WORK_DIR)/testdata
-      export MODELS_PATH=$(WORK_DIR)/testdata
-      export LD_LIBRARY_PATH=$(BIN_DIR)/lib
-      export PYTHONPATH=$(BIN_DIR)/lib/python_api/python3.6
-      env
-      cd $(REPO_DIR)/inference-engine/ie_bridges/python/tests
-      pytest
-    displayName: 'Python API Tests'
-    continueOnError: false
-    enabled: false
-

.ci/azure/mac.yml

@@ -1,102 +0,0 @@
-jobs:
-- job: Mac
-  # About 200% of total time (perfomace of Mac hosts is unstable)
-  timeoutInMinutes: 180
-  pool:
-    vmImage: 'macOS-10.15'
-  variables:
-    system.debug: true
-    WORKERS_NUMBER: 3
-    BUILD_TYPE: Release
-    REPO_DIR: $(Build.Repository.LocalPath)
-    WORK_DIR: $(Pipeline.Workspace)/_w
-    BUILD_DIR: $(WORK_DIR)/build
-    BIN_DIR: $(REPO_DIR)/bin/intel64/$(BUILD_TYPE)
-  steps:
-  - checkout: self
-    clean: true
-    fetchDepth: 1
-    lfs: false
-    submodules: recursive
-    path: openvino
-  - script: |
-      whoami
-      uname -a
-      which python3
-      python3 --version
-      gcc --version
-      xcrun --sdk macosx --show-sdk-version
-      env
-      sysctl -a
-    displayName: 'System info'
-  - script: |
-      rm -rf $(WORK_DIR) ; mkdir $(WORK_DIR)
-      rm -rf $(BUILD_DIR) ; mkdir $(BUILD_DIR)
-    displayName: 'Make dir'
-  - task: UsePythonVersion@0
-    inputs:
-      versionSpec: '3.7'
-  - script: |
-      brew install cython
-      brew install automake
-    displayName: 'Install dependencies'
-  - script: brew install ninja
-    displayName: 'Install Ninja'
-  - script: |
-      export PATH="/usr/local/opt/cython/bin:$PATH"
-      export CC=gcc
-      export CXX=g++
-      # Disable errors with Ninja
-      export CXXFLAGS="-Wno-error=unused-command-line-argument"
-      export CFLAGS="-Wno-error=unused-command-line-argument"
-      cmake -GNinja -DVERBOSE_BUILD=ON -DCMAKE_BUILD_TYPE=$(BUILD_TYPE) -DENABLE_PYTHON=ON -DENABLE_TESTS=ON $(REPO_DIR)
-    workingDirectory: $(BUILD_DIR)
-    displayName: 'CMake'
-  - script: ninja
-    workingDirectory: $(BUILD_DIR)
-    displayName: 'Build Mac'
-  - script: ls -alR $(REPO_DIR)/bin/
-    displayName: 'List files'
-  - script: $(BIN_DIR)/unit-test --gtest_print_time=1 --gtest_filter=-backend_api.config_unsupported:*IE_GPU*:IE_CPU.onnx_model_sigmoid
-    displayName: 'nGraph UT'
-    continueOnError: false
-  - script: $(BIN_DIR)/InferenceEngineUnitTests --gtest_print_time=1
-    displayName: 'IE UT old'
-    continueOnError: false
-  - script: $(BIN_DIR)/ieUnitTests
-    displayName: 'IE UT'
-    continueOnError: false
-  - script: $(BIN_DIR)/cpuUnitTests
-    displayName: 'CPU UT'
-    continueOnError: false
-  - script: $(BIN_DIR)/vpuUnitTests
-    displayName: 'VPU UT'
-    continueOnError: false
-  - script: $(BIN_DIR)/ieFuncTests
-    displayName: 'IE FuncTests'
-    continueOnError: false
-  - script: $(BIN_DIR)/cpuFuncTests --gtest_print_time=1
-    displayName: 'CPU FuncTests'
-    continueOnError: false
-  - script: $(BIN_DIR)/MklDnnBehaviorTests
-    displayName: 'MklDnnBehaviorTests'
-    continueOnError: false
-  - script: |
-      git clone https://github.com/openvinotoolkit/testdata.git
-      git clone https://github.com/google/gtest-parallel.git
-    workingDirectory: $(WORK_DIR)
-    displayName: 'Clone testdata & gtest-parallel'
-  - script: |
-      export DATA_PATH=$(WORK_DIR)/testdata
-      export MODELS_PATH=$(WORK_DIR)/testdata
-      python3 $(WORK_DIR)/gtest-parallel/gtest-parallel $(BIN_DIR)/MklDnnFunctionalTests --workers=$(WORKERS_NUMBER) --print_test_times --dump_json_test_results=MklDnnFunctionalTests.json --gtest_filter=-smoke_MobileNet/ModelTransformationsTest.LPT/mobilenet_v2_tf_depthwise_batch1_inPluginDisabled_inTestDisabled_asymmetric* -- --gtest_print_time=1
-    workingDirectory: $(WORK_DIR)
-    displayName: 'MklDnnFunctionalTests'
-    continueOnError: false
-  - script: |
-      export DATA_PATH=$(WORK_DIR)/testdata
-      export MODELS_PATH=$(WORK_DIR)/testdata
-      $(BIN_DIR)/InferenceEngineCAPITests
-    displayName: 'IE CAPITests'
-    continueOnError: false
-

.ci/azure/windows.yml

@@ -1,133 +0,0 @@
-jobs:
-- job: Win
-  # About 150% of total time
-  timeoutInMinutes: 120
-  pool:
-    name: WIN_VMSS_VENV_F8S_WU2
-  variables:
-    system.debug: true
-    WORKERS_NUMBER: 8
-    BUILD_TYPE: Release
-    REPO_DIR: $(Build.Repository.LocalPath)
-    WORK_DIR: $(Pipeline.Workspace)\_w
-    BUILD_DIR: D:\build
-    BIN_DIR: $(REPO_DIR)\bin\intel64
-    MSVS_VARS_PATH: C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise\VC\Auxiliary\Build\vcvars64.bat
-    MSVC_COMPILER_PATH: C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise\VC\Tools\MSVC\14.24.28314\bin\Hostx64\x64\cl.exe
-  steps:
-  - checkout: self
-    clean: true
-    fetchDepth: 1
-    lfs: false
-    submodules: recursive
-    path: openvino
-  - script: |
-      powershell -command "Invoke-RestMethod -Headers @{\"Metadata\"=\"true\"} -Method GET -Uri http://169.254.169.254/metadata/instance/compute?api-version=2019-06-01 | format-custom"
-      where python3
-      where python
-      python --version
-      wmic computersystem get TotalPhysicalMemory
-      wmic cpu list
-      wmic logicaldisk get description,name
-      wmic VOLUME list
-      set
-    displayName: 'System info'
-  - script: |
-      rd /Q /S $(WORK_DIR) & mkdir $(WORK_DIR)
-      rd /Q /S $(BUILD_DIR) & mkdir $(BUILD_DIR)
-    displayName: 'Make dir'
-  - script: |
-      certutil -urlcache -split -f https://github.com/ninja-build/ninja/releases/download/v1.10.0/ninja-win.zip ninja-win.zip
-      powershell -command "Expand-Archive -Force ninja-win.zip"
-    workingDirectory: $(WORK_DIR)
-    displayName: Install Ninja
-  - script: |
-      certutil -urlcache -split -f https://incredibuilddiag1wu2.blob.core.windows.net/incredibuild/IBSetupConsole_9_5_0.exe IBSetupConsole_9_5_0.exe
-      call IBSetupConsole_9_5_0.exe /Install /Components=Agent,oneuse /Coordinator=11.1.0.4 /AGENT:OPENFIREWALL=ON /AGENT:AUTOSELECTPORTS=ON /ADDTOPATH=ON /AGENT:INSTALLADDINS=OFF
-    workingDirectory: $(WORK_DIR)
-    displayName: Install IncrediBuild
-  - script: |
-      echo Stop IncrediBuild_Agent && net stop IncrediBuild_Agent
-      reg add HKEY_LOCAL_MACHINE\SOFTWARE\Wow6432Node\Xoreax\IncrediBuild\Builder /f /v LastEnabled /d 0 && echo Start IncrediBuild_Agent && net start IncrediBuild_Agent
-    displayName: Start IncrediBuild
-  - script: |
-      set PATH=$(WORK_DIR)\ninja-win;%PATH%
-      call "$(MSVS_VARS_PATH)" && cmake -GNinja -DCMAKE_BUILD_TYPE=$(BUILD_TYPE) -DENABLE_TESTS=ON -DCMAKE_C_COMPILER:PATH="$(MSVC_COMPILER_PATH)" -DCMAKE_CXX_COMPILER:PATH="$(MSVC_COMPILER_PATH)" $(REPO_DIR)
-    workingDirectory: $(BUILD_DIR)
-    displayName: 'CMake'
-  - script: |
-      set PATH=$(WORK_DIR)\ninja-win;%PATH%
-      call "$(MSVS_VARS_PATH)" && "C:\Program Files (x86)\IncrediBuild\BuildConsole.exe" /COMMAND="ninja" /MaxCPUS=40
-    workingDirectory: $(BUILD_DIR)
-    displayName: 'Build Win'
-  - script: echo Stop IncrediBuild_Agent && net stop IncrediBuild_Agent
-    displayName: Stop IncrediBuild
-    continueOnError: true
-  - script: dir $(REPO_DIR)\bin\ /s /b
-    displayName: 'List files'
-  - script: |
-      set PATH=$(REPO_DIR)\inference-engine\temp\tbb\bin;%PATH%
-      $(BIN_DIR)\unit-test --gtest_print_time=1 --gtest_filter=-backend_api.config_unsupported:*IE_GPU*
-    displayName: 'nGraph UT'
-    continueOnError: false
-  - script: |
-      set PATH=$(REPO_DIR)\inference-engine\temp\tbb\bin;%PATH%
-      $(BIN_DIR)\InferenceEngineUnitTests --gtest_print_time=1
-    displayName: 'IE UT old'
-    continueOnError: false
-  - script: |
-      set PATH=$(REPO_DIR)\inference-engine\temp\tbb\bin;%PATH%
-      $(BIN_DIR)\ieUnitTests
-    displayName: 'IE UT'
-    continueOnError: false
-  - script: |
-      set PATH=$(REPO_DIR)\inference-engine\temp\tbb\bin;%PATH%
-      $(BIN_DIR)\cpuUnitTests
-    displayName: 'CPU UT'
-    continueOnError: false
-  - script: |
-      set PATH=$(REPO_DIR)\inference-engine\temp\tbb\bin;%PATH%
-      $(BIN_DIR)\gnaUnitTests
-    displayName: 'GNA UT'
-    continueOnError: false
-  - script: |
-      set PATH=$(REPO_DIR)\inference-engine\temp\tbb\bin;%PATH%
-      $(BIN_DIR)\vpuUnitTests
-    displayName: 'VPU UT'
-    continueOnError: false
-  - script: |
-      set PATH=$(REPO_DIR)\inference-engine\temp\tbb\bin;%PATH%
-      $(BIN_DIR)\ieFuncTests
-    displayName: 'IE FuncTests'
-    continueOnError: false
-  - script: |
-      set PATH=$(REPO_DIR)\inference-engine\temp\tbb\bin;%PATH%
-      $(BIN_DIR)\cpuFuncTests --gtest_print_time=1
-    displayName: 'CPU FuncTests'
-    continueOnError: false
-  - script: |
-      set PATH=$(REPO_DIR)\inference-engine\temp\tbb\bin;%PATH%
-      $(BIN_DIR)\MklDnnBehaviorTests
-    displayName: 'MklDnnBehaviorTests'
-    continueOnError: false
-  - script: |
-      git clone https://github.com/openvinotoolkit/testdata.git
-      git clone https://github.com/google/gtest-parallel.git
-    workingDirectory: $(BUILD_DIR)
-    displayName: 'Clone testdata & gtest-parallel'
-    # Add for gtest-parallel, it hangs now (CVS-33386)
-    #python $(BUILD_DIR)\gtest-parallel\gtest-parallel $(BIN_DIR)\MklDnnFunctionalTests --workers=$(WORKERS_NUMBER) --print_test_times --dump_json_test_results=MklDnnFunctionalTests.json -- --gtest_print_time=1
-  - script: |
-      set PATH=$(REPO_DIR)\inference-engine\temp\tbb\bin;$(REPO_DIR)\inference-engine\temp\opencv_4.5.0\opencv\bin;%PATH%
-      set DATA_PATH=$(BUILD_DIR)\testdata
-      set MODELS_PATH=$(BUILD_DIR)\testdata
-      $(BIN_DIR)\MklDnnFunctionalTests --gtest_print_time=1
-    displayName: 'MklDnnFunctionalTests'
-    continueOnError: false
-  - script: |
-      set PATH=$(REPO_DIR)\inference-engine\temp\tbb\bin;$(REPO_DIR)\inference-engine\temp\opencv_4.5.0\opencv\bin;%PATH%
-      set DATA_PATH=$(BUILD_DIR)\testdata
-      set MODELS_PATH=$(BUILD_DIR)\testdata
-      $(BIN_DIR)\InferenceEngineCAPITests
-    displayName: 'IE CAPITests'
-    continueOnError: false

.ci/openvino-onnx/Dockerfile

@@ -1,83 +0,0 @@
-FROM ubuntu:20.04
-
-LABEL version=2020.07.09.1
-
-ARG http_proxy
-ARG https_proxy
-ENV http_proxy ${http_proxy}
-ENV https_proxy ${https_proxy}
-
-ENV CI=true
-ENV DEBIAN_FRONTEND=noninteractive
-ENV PYTHONUNBUFFERED 1
-
-# Install base dependencies
-RUN apt-get update && apt-get install -y locales && apt-get clean autoclean && apt-get autoremove -y
-
-# Set the locale to en_US.UTF-8
-RUN locale-gen en_US.UTF-8
-ENV LANG en_US.UTF-8
-ENV LANGUAGE en_US:en
-ENV LC_ALL en_US.UTF-8
-
-RUN apt-get update && apt-get -y --no-install-recommends install \
-# OpenVINO dependencies
-        autoconf \
-        automake \
-        build-essential \
-        cmake \
-        curl \
-        git \
-        libtool \
-        ocl-icd-opencl-dev \
-        pkg-config \
-        unzip \
-        wget \
-# Python dependencies
-        python3 \
-        python3-pip \
-        python3-dev \
-        python3-virtualenv \
-        cython3 \
-        tox \
-# ONNX dependencies
-        git-lfs \
-        protobuf-compiler \
-        libprotobuf-dev && \
-    apt-get clean autoclean && \
-    apt-get autoremove -y
-
-# Build OpenVINO
-COPY . /openvino/
-WORKDIR /openvino/build
-RUN cmake .. \
-    -DCMAKE_BUILD_TYPE=Release \
-    -DENABLE_VPU=OFF \
-    -DENABLE_GNA=OFF \
-    -DENABLE_OPENCV=OFF \
-    -DENABLE_CPPLINT=OFF \
-    -DENABLE_TESTS=OFF \
-    -DENABLE_BEH_TESTS=OFF \
-    -DENABLE_FUNCTIONAL_TESTS=OFF \
-    -DENABLE_MKL_DNN=ON \
-    -DENABLE_CLDNN=OFF \
-    -DENABLE_PROFILING_ITT=OFF \
-    -DENABLE_SAMPLES=OFF \
-    -DENABLE_SPEECH_DEMO=OFF \
-    -DENABLE_PYTHON=ON \
-    -DPYTHON_EXECUTABLE=/usr/bin/python3 \
-    -DNGRAPH_ONNX_IMPORT_ENABLE=ON \
-    -DNGRAPH_INTERPRETER_ENABLE=ON \
-    -DNGRAPH_DEBUG_ENABLE=OFF \
-    -DNGRAPH_DYNAMIC_COMPONENTS_ENABLE=ON \
-    -DCMAKE_INSTALL_PREFIX=/openvino/dist
-RUN make -j $(nproc) install
-
-# Run tests via tox
-WORKDIR /openvino/ngraph/python
-ENV NGRAPH_CPP_BUILD_PATH=/openvino/dist
-ENV LD_LIBRARY_PATH=/openvino/dist/lib
-ENV NGRAPH_ONNX_IMPORT_ENABLE=TRUE
-ENV PYTHONPATH=/openvino/bin/intel64/Release/lib/python_api/python3.8:${PYTHONPATH}
-RUN git clone --recursive https://github.com/pybind/pybind11.git -b v2.5.0 --depth 1
-CMD tox

.ci/openvino-onnx/Jenkinsfile

@@ -1,129 +0,0 @@
-// Copyright (C) 2018-2020 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-
-DOCKER_CONTAINER_NAME= "openvino-onnx-ci-container"
-DOCKER_IMAGE_TAG = "openvino-onnx-ci-image"
-
-def getGitPrInfo(String project) {
-    def gitPrInfo = [
-        prAuthorEmail : "",
-        commitAuthorEmail : "",
-        commitHash : "",
-        commitSubject : ""
-    ]
-    try {
-        dir ("${WORKDIR}/${project}") {
-            gitPrInfo.prAuthorEmail = sh (script: 'git log -1 --pretty="format:%ae" ', returnStdout: true).trim()
-            gitPrInfo.commitAuthorEmail = sh (script: 'git log -1 --pretty="format:%ce" ', returnStdout: true).trim()
-            gitPrInfo.commitSubject = sh (script: 'git log -1 --pretty="format:%H" ', returnStdout: true).trim()
-            gitPrInfo.commitHash = sh (script: 'git log -1 --pretty="format:%s" ', returnStdout: true).trim()
-        }
-    }
-    catch(e) {
-        echo "Failed to retrieve ${project} git repository information!"
-        echo "ERROR: ${e}"
-    }
-    return gitPrInfo
-}
-
-def notifyByEmail(def gitPrInfo) {
-    stage('Notify') {
-        String notifyPeople = "${gitPrInfo.prAuthorEmail}, ${gitPrInfo.commitAuthorEmail}"
-        emailext (
-            subject: "OpenVino CI: PR ${CHANGE_ID} ${currentBuild.result}!",
-            body: """
-                    Status: ${currentBuild.result}
-                    Pull Request Title: ${CHANGE_TITLE}
-                    Pull Request: ${CHANGE_URL}
-                    Branch: ${CHANGE_BRANCH}
-                    Commit Hash: ${gitPrInfo.commitSubject}
-                    Commit Subject: ${gitPrInfo.commitHash}
-                    Jenkins Build: ${RUN_DISPLAY_URL}
-            """,
-            to: "${notifyPeople}"
-        )
-    }
-}
-
-def gitSubmoduleUpdate(String repository_name) {
-    dir ("${WORKDIR}/${repository_name}") {
-        sh  label: "Init ${repository_name} submodules",
-            script:
-        """
-            git submodule init && git submodule update \
-                --init \
-                --no-fetch \
-                --recursive 
-        """
-    }
-}
-
-def buildDockerImage() {
-    sh """
-        docker build --tag=${DOCKER_IMAGE_TAG}  --file=.ci/openvino-onnx/Dockerfile \
-        --build-arg http_proxy=http://proxy-chain.intel.com:911/ \
-        --build-arg https_proxy=http://proxy-chain.intel.com:912/ .
-    """
-}
-
-def runTests() {
-    sh """
-        docker run --name ${DOCKER_CONTAINER_NAME} \
-        --volume ${HOME}/ONNX_CI/onnx_models/.onnx:/root/.onnx ${DOCKER_IMAGE_TAG}
-    """
-}
-
-pipeline {
-    agent {
-        label "OpenVino"
-    }
-    environment {
-        PROJECT_NAME = "openvino"
-        WORKDIR = "${WORKSPACE}/${BUILD_NUMBER}"
-    }
-    options {
-        skipDefaultCheckout true
-    }
-    stages {
-        stage("Clone repository") {
-            steps{
-                dir("${WORKDIR}") {
-                    checkout scm
-                }
-                gitSubmoduleUpdate(PROJECT_NAME)
-            }
-        }
-        stage("Prepare Docker environment") {
-            steps{
-                dir("${WORKDIR}") {
-                    buildDockerImage()
-                }
-            }
-        }
-        stage("Run tests") {
-            options {
-                  timeout(time: 10, unit: 'MINUTES')
-              }
-            steps{
-                runTests()
-            }
-        }
-    }
-    post {
-        failure {
-            script {
-                gitPrInfo = getGitPrInfo(PROJECT_NAME)
-                notifyByEmail(gitPrInfo)
-            }
-        }
-        cleanup {
-            dir("${WORKDIR}") {
-                deleteDir()
-                sh """
-                    docker image prune -f
-                    docker rm -f ${DOCKER_CONTAINER_NAME}
-                """
-            }
-        }
-    }
-}

.github/ISSUE_TEMPLATE/bug.md

@@ -1,58 +0,0 @@
----
-name: Bug
-about: Create a report to help us improve
-title: "[Bug]"
-labels: bug, support_request
-assignees: ''
-
----
-
-##### System information (version)
-<!-- Example
-- OpenVINO => 2020.4
-- Operating System / Platform => Windows 64 Bit
-- Compiler => Visual Studio 2017
-- Problem classification: Model Conversion
-- Framework: TensorFlow (if applicable)
-- Model name: ResNet50 (if applicable)
--->
-
-- OpenVINO=> :grey_question:
-- Operating System / Platform => :grey_question:
-- Compiler => :grey_question:
-- Problem classification => :grey_question:
-
-##### Detailed description
-<!-- your description -->
-
-##### Steps to reproduce
-
-<!--
-   Describe your problem and steps you've done before you got to this point.    
-   to add code example fence it with triple backticks and optional file extension
-    ```.cpp
-    // C++ code example
-    ```
- or attach as .txt or .zip file
--->
-
-##### Issue submission checklist
-
- - [ ] I report the issue, it's not a question
-   <!--
-   OpenVINO team works with support forum, Stack Overflow and other communities
-   to discuss problems. Tickets with question without real issue statement will be
-   closed.
-   -->
- - [ ] I checked the problem with documentation, FAQ, open issues, Stack Overflow, etc and have not found solution
-   <!--
-   Places to check:
-   * OpenVINO documentation: https://docs.openvinotoolkit.org/
-   * OpenVINO forum: https://community.intel.com/t5/Intel-Distribution-of-OpenVINO/bd-p/distribution-openvino-toolkit
-   * OpenVINO issue tracker: https://github.com/openvinotoolkit/openvino/issues?q=is%3Aissue
-   * Stack Overflow branch: https://stackoverflow.com/questions/tagged/openvino
-   -->
- - [ ] There is reproducer code and related data files: images, videos, models, etc.
-   <!--
-   The best reproducer -- test case for OpenVINO that we can add to the library.
-   -->

.github/org_control/check_org.py

@@ -1,51 +0,0 @@
-# Copyright (C) 2020 Intel Corporation
-# SPDX-License-Identifier: Apache-2.0
-
-"""
-Check GitHub organization and invite members
-"""
-
-# pylint: disable=fixme,no-member
-
-from argparse import ArgumentParser
-
-import github_api
-from configs import Config
-
-
-def main():
-    """The main entry point function"""
-    arg_parser = ArgumentParser()
-    arg_parser.add_argument("--cfg-file", metavar="PATH", default=Config.default_cfg_path,
-                            help=f"Path to json configuration file, e.g. {Config.default_cfg_path}")
-    arg_parser.add_argument("--teams", action="store_true", help="Check GitHub teams")
-    args, unknown_args = arg_parser.parse_known_args()
-
-    Config(args.cfg_file, unknown_args)
-    gh_api = github_api.GithubOrgApi()
-
-    if args.teams:
-        gh_api.get_org_teams()
-    else:
-        dev_emails = github_api.get_dev_emails()
-        print(f'\nDeveloper emails {len(dev_emails)}:', '; '.join(dev_emails))
-
-        org_emails = gh_api.get_org_emails()
-        print(f'\nOrg emails {len(org_emails)}:', '; '.join(org_emails))
-
-        org_pendig_invitation_emails = gh_api.get_org_invitation_emails()
-
-        invite_emails = dev_emails.difference(org_emails).difference(org_pendig_invitation_emails)
-        print(f'\nInvite emails {len(invite_emails)}:', '; '.join(invite_emails))
-
-        no_in_dev_emails = org_emails.difference(dev_emails)
-        print(f'\nOrg members - no in developers list {len(no_in_dev_emails)}:',
-              '; '.join(no_in_dev_emails))
-
-        valid_github_users = gh_api.get_valid_github_users(invite_emails)
-
-        gh_api.invite_users(valid_github_users)
-
-
-if __name__ == '__main__':
-    main()

.github/org_control/check_pr.py

@@ -1,149 +0,0 @@
-# Copyright (C) 2020 Intel Corporation
-# SPDX-License-Identifier: Apache-2.0
-
-"""
-Check GitHub PRs and set labels by type and categories, e.g. 'ExternalPR', 'category: ci'
-"""
-
-# pylint: disable=fixme,no-member
-
-import re
-import datetime
-from argparse import ArgumentParser
-from enum import Enum
-
-import github_api
-from configs import Config
-
-
-class PrType(Enum):
-    """Constants for type of GitHub pull request by author membership"""
-    EXTERNAL = 'ExternalPR'
-    INTEL = 'ExternalIntelPR'
-    ORG = 'OpenvinoPR'
-    BAD = 'BadPR'
-
-
-def get_pr_labels(pull):
-    """Gets PR labels as set"""
-    pr_lables = set()
-    for label in pull.labels:
-        pr_lables.add(label.name)
-    return pr_lables
-
-
-def set_pr_labels(pull, labels):
-    """Sets PR labels"""
-    if not labels or Config().DRY_RUN:
-        return
-    print(f'Set PR labels:', labels)
-    pull.set_labels(labels)
-
-
-def get_pr_type_by_labels(pull):
-    """Gets PR type using labels"""
-    pr_lables = get_pr_labels(pull)
-    pr_types = set(type.value for type in PrType)
-    pr_types_labels = pr_lables & pr_types
-    if not pr_types_labels:
-        return None
-    if len(pr_types_labels) > 1:
-        print(f'Duplicated labels: {pr_types_labels}')
-        return PrType.BAD
-    return PrType(PrType(pr_types_labels.pop()))
-
-
-def get_label_by_team_name_re(team_name):
-    """Generates label by PR reviwer team name using regular expressions"""
-    if 'admins' in team_name:
-        return 'category: ci'
-    re_compile_label = re.compile(rf'{Config().GITHUB_REPO}-(.+)-maintainers')
-    re_label = re_compile_label.match(team_name)
-    if re_label:
-        return f'category: {re_label.group(1).strip()}'
-    return None
-
-
-def get_label_by_team_name_map(team_name):
-    """Generates label by PR reviwer team name using config map"""
-    return Config().TEAM_TO_LABEL.get(team_name)
-
-
-def get_category_labels(pull):
-    """Gets list of category labels by all PR reviwer teams"""
-    labels = []
-    pr_lables = get_pr_labels(pull)
-    for reviewer_team in pull.get_review_requests()[1]:
-        reviewer_label = get_label_by_team_name_map(reviewer_team.name)
-        if reviewer_label and reviewer_label not in pr_lables:
-            labels.append(reviewer_label)
-    return labels
-
-
-def main():
-    """The main entry point function"""
-    arg_parser = ArgumentParser()
-    arg_parser.add_argument("--cfg-file", metavar="PATH", default=Config.default_cfg_path,
-                            help=f"Path to json configuration file, e.g. {Config.default_cfg_path}")
-    arg_parser.add_argument("--pr", metavar="NUMBER",
-                            help="Get GitHub pull request with the number")
-    arg_parser.add_argument("--pr-state", default="open", choices=["open", "closed"],
-                            help="Set GitHub pull request state")
-    arg_parser.add_argument("--newer", metavar="MINUTES",
-                            help="Get newly created GitHub pull request only")
-    args, unknown_args = arg_parser.parse_known_args()
-
-    Config(args.cfg_file, unknown_args)
-    gh_api = github_api.GithubOrgApi()
-
-    if args.pr:
-        pulls = [gh_api.repo.get_pull(int(args.pr))]
-    else:
-        pulls = gh_api.repo.get_pulls(state=args.pr_state)
-        print(f'\nPRs count ({args.pr_state}):', pulls.totalCount)
-
-    if args.newer:
-        pr_created_after = datetime.datetime.now() - datetime.timedelta(minutes=int(args.newer))
-        print('PRs created after:', pr_created_after)
-    non_org_intel_pr_users = set()
-    non_org_pr_users = set()
-    for pull in pulls:
-        if args.newer and pull.created_at <= pr_created_after:
-            print(f'\nIGNORE: {pull} - Created: {pull.created_at}')
-            continue
-        pr_lables = get_pr_labels(pull)
-        pr_type_by_labels = get_pr_type_by_labels(pull)
-        set_labels = []
-        print(f'\n{pull} - Created: {pull.created_at} - Labels: {pr_lables} -',
-              f'Type: {pr_type_by_labels}', end='')
-
-        # Checks PR source type
-        if gh_api.is_org_user(pull.user):
-            print(' - Org user')
-        elif github_api.is_intel_email(pull.user.email) or \
-             github_api.is_intel_company(pull.user.company):
-            print(' - Non org user with Intel email or company')
-            non_org_intel_pr_users.add(pull.user)
-            if pr_type_by_labels is not PrType.INTEL:
-                print(f'NO "{PrType.INTEL.value}" label: ', end='')
-                github_api.print_users(pull.user)
-                set_labels.append(PrType.INTEL.value)
-        else:
-            print(f' - Non org user with NO Intel email or company')
-            non_org_pr_users.add(pull.user)
-            if pr_type_by_labels is not PrType.EXTERNAL:
-                print(f'NO "{PrType.EXTERNAL.value}" label: ', end='')
-                github_api.print_users(pull.user)
-                set_labels.append(PrType.EXTERNAL.value)
-
-        set_labels += get_category_labels(pull)
-        set_pr_labels(pull, set_labels)
-
-    print(f'\nNon org user with Intel email or company:')
-    github_api.print_users(non_org_intel_pr_users)
-    print(f'\nNon org user with NO Intel email or company:')
-    github_api.print_users(non_org_pr_users)
-
-
-if __name__ == '__main__':
-    main()

.github/org_control/config.json

@@ -1,36 +0,0 @@
-{
-    "GITHUB_TOKEN": "<Put token here or set as arg or as env variable>",
-    "GITHUB_ORGANIZATION": "openvinotoolkit",
-    "GITHUB_REPO": "openvino",
-    "IGNORE_LOGINS": [
-        "openvino-ci",
-        "openvino-pushbot",
-        "lab-nerval",
-        "lab-nerval-onnx-ci"
-    ],
-    "EMAILS_FILE_PATH": "dev_emails-test.txt",
-    "PROXIES": {
-        "HTTP_PROXY": null,
-        "HTTPS_PROXY": null,
-        "NO_PROXY": "localhost,127.0.0.1,.intel.com"
-    },
-    "DRY_RUN": false,
-    "TEAM_TO_LABEL": {
-        "openvino-admins": "category: CI",
-        "openvino-maintainers": "category: IE common",
-        "openvino-docs-maintainers": "category: docs",
-        "openvino-ie-maintainers": "category: IE common",
-        "openvino-ie-cpu-maintainers": "category: CPU",
-        "openvino-ie-gna-maintainers": "category: GNA",
-        "openvino-ie-gpu-maintainers": "category: GPU",
-        "openvino-ie-lpt-maintainers": "category: LP transformations",
-        "openvino-ie-multi-maintainers": "category: MULTI",
-        "openvino-ie-python-api-maintainers": "category: python api",
-        "openvino-ie-tests-maintainers": "category: IE Tests",
-        "openvino-ie-vpu-maintainers": "category: VPU",
-        "openvino-mo-maintainers": "category: MO",
-        "openvino-ngraph-maintainers": "category: nGraph",
-        "openvino-tests-maintainers": "category: IE Tests",
-        "openvino-tools-maintainers": "category: tools"
-    }
-}

.github/org_control/configs.py

@@ -1,113 +0,0 @@
-# Copyright (C) 2020 Intel Corporation
-# SPDX-License-Identifier: Apache-2.0
-
-"""
-Configurations management
-"""
-
-# pylint: disable=fixme,broad-except
-
-import os
-import sys
-import ast
-import json
-from pathlib import Path
-
-
-if sys.hexversion < 0x3060000:
-    raise Exception('Python version must be >= 3.6')
-
-
-class ConfigException(Exception):
-    """Base configuration exception"""
-
-
-class Config:
-    """Configuration wrapper"""
-    _instance = None
-    properties = None
-    default_cfg_path = Path(__file__).resolve().parent / 'config.json'
-
-    def __new__(cls, *_args, **_kwargs):
-        if not Config._instance:
-            Config._instance = super(Config, cls).__new__(cls)
-        return Config._instance
-
-    def __init__(self, file_path=None, cli_args=None):
-        """
-        :param file_path: Path to json configuration file
-        :type file_path: String
-
-        :param args: List of argparse arguments with patterns: 'name=value' or 'name'
-        :type args: list
-        """
-        if Config.properties:
-            return
-
-        self._file_path = file_path or Config.default_cfg_path
-        self._cli_args = cli_args or []
-
-        self._json_cfg = {}
-        self._args = {}
-
-        self._load_cfg()
-        self._parse_cli_args()
-
-        Config.properties = {}
-        for name, value in self._json_cfg.items():
-            if hasattr(self, name):
-                raise ConfigException(f'Duplicating prosperity: {name}')
-            prosperity_value = self._args.get(name) or os.getenv(name)
-            if prosperity_value:
-                # Try to set prosperity_value as Python literal structures, e.g. DRY_RUN=False
-                try:
-                    prosperity_value = ast.literal_eval(prosperity_value)
-                except Exception:
-                    pass
-                if not isinstance(prosperity_value, type(value)):
-                    raise ConfigException(f'Python type of {name} parameter must be {type(value)}')
-            else:
-                prosperity_value = value
-            setattr(self, name, prosperity_value)
-            Config.properties[name] = prosperity_value
-
-        self.set_proxy()
-
-    def _load_cfg(self):
-        """Load the json configuration file"""
-        try:
-            with open(self._file_path) as conf:
-                self._json_cfg = json.load(conf)
-        except:
-            print('Failed to load configuration from:', self._file_path)
-            raise
-
-    def _parse_cli_args(self):
-        """Parse argparse arguments with patterns: 'name=value' or 'name'"""
-        for cli_arg in self._cli_args:
-            arg = cli_arg.split('=')
-            if arg[0] not in self._json_cfg:
-                raise ConfigException(f'Unsupported argument: {arg}')
-            self._args[arg[0]] = True if len(arg) == 1 else '='.join(arg[1:])
-
-    def get_properties(self):
-        """Get all properties as Dict"""
-        return self.properties
-
-    def set_proxy(self):
-        """Set proxies"""
-        for proxy_name, url in self.properties['PROXIES'].items():
-            if url is not None:
-                print(f'Set proxy: {proxy_name}={url}')
-                os.environ[proxy_name] = url
-
-
-def _test():
-    """Test and debug"""
-    print('Config.default_cfg_path:', Config.default_cfg_path)
-    cfg = Config(cli_args=['DRY_RUN=True'])
-    print('Config.properties:', cfg.get_properties())
-
-
-if __name__ == '__main__':
-    _test()

.github/org_control/dev_emails-test.txt

@@ -1,9 +0,0 @@
-# good comment
-Last_name, First_name <first_name.last_name@intel.com>
-first_name.last_name@intel.com
-openvino_pushbot@intel.com
-
-# Wrong emails
-foo@foo.com
-  foo1  foo2
-foo1 foo2@intel.com

.github/org_control/github_api.py

@@ -1,287 +0,0 @@
-# Copyright (C) 2020 Intel Corporation
-# SPDX-License-Identifier: Apache-2.0
-
-"""
-GitHub API for controlling organization
-"""
-
-# pylint: disable=fixme,no-member
-
-import re
-import time
-
-from github import Github, GithubException, RateLimitExceededException, IncompletableObject
-from github import UnknownObjectException
-from github.PaginatedList import PaginatedList
-
-from configs import Config
-
-
-def is_valid_user(user):
-    """Checks that user is valid github.Github object"""
-    try:
-        return user and user.login
-    except IncompletableObject:
-        return False
-
-
-def is_user_ignored(user):
-    """Checks that user should be ignored"""
-    cfg = Config()
-    if is_valid_user(user) and user.login.lower() not in cfg.properties['IGNORE_LOGINS']:
-        return False
-    return True
-
-
-def is_valid_name(name):
-    """Checks that GitHub user's name is valid"""
-    return name and len(name) >= 3 and ' ' in name
-
-
-def is_intel_email(email):
-    """Checks that email is valid Intel email"""
-    return email and len(email) > 10 and ' ' not in email and email.lower().endswith('@intel.com')
-
-
-def is_intel_company(company):
-    """Checks that company contains intel"""
-    return company and 'intel' in company.lower()
-
-
-def is_valid_intel_user(user):
-    """Checks that user is valid GitHub and Intel user"""
-    return is_valid_user(user) and (is_valid_name(user.name) and is_intel_email(user.email) or
-                                    is_user_ignored(user))
-
-
-def print_users(users):
-    """Print list of users in different formats: list, set, PaginatedList"""
-    if isinstance(users, (list, set, PaginatedList)):
-        users_count = users.totalCount if isinstance(users, PaginatedList) else len(users)
-        print(f'\nGitHub users {users_count} (login - name - company - email - valid):')
-    else:
-        users = [users]
-    for user in users:
-        if not is_valid_user(user):
-            print('WRONG GitHub user: ???')
-            continue
-        valid_check = 'OK' if is_valid_intel_user(user) else 'FIX'
-        if not is_intel_email(user.email):
-            valid_check += ' email'
-        if not is_valid_name(user.name):
-            valid_check += ' name'
-        print(f'{user.login} - "{user.name}" - "{user.company}" - {user.email} - {valid_check}')
-
-
-def get_dev_emails():
-    """
-    Read a file with developer emails. Supported email formats
-    first_name.last_name@intel.com
-    Import from Outlook: Last_name, First_name <first_name.last_name@intel.com>
-    """
-    re_email = re.compile(r'.+<(.+)>')
-    emails = set()
-    cfg = Config()
-    with open(cfg.properties['EMAILS_FILE_PATH']) as file_obj:
-        for line in file_obj:
-            line = line.strip().lower()
-            if not line or line.startswith('#'):
-                continue
-            re_outlook_email = re_email.match(line)
-            if re_outlook_email:
-                line = re_outlook_email.group(1).strip()
-            if not is_intel_email(line):
-                print(f'Wrong email in {cfg.properties["EMAILS_FILE_PATH"]}: {line}')
-                continue
-            emails.add(line)
-    return emails
-
-
-class GithubOrgApi:
-    """Common API for GitHub organization"""
-
-    def __init__(self):
-        self._cfg = Config()
-        self.github = Github(self._cfg.GITHUB_TOKEN)
-        self.github_org = self.github.get_organization(self._cfg.GITHUB_ORGANIZATION)
-        self.repo = self.github.get_repo(f'{self._cfg.GITHUB_ORGANIZATION}/'
-                                         f'{self._cfg.GITHUB_REPO}')
-
-    def is_org_user(self, user):
-        """Checks that user is a member of GitHub organization"""
-        if is_valid_user(user):
-            try:
-                membership = user.get_organization_membership(self.github_org)
-                # membership.role can be 'member' or 'admin'
-                if membership.state == 'active' and membership.role:
-                    return True
-            except UnknownObjectException:
-                pass
-        return False
-
-    def get_org_emails(self):
-        """Gets and prints all emails of GitHub organization members"""
-        org_members = self.github_org.get_members()
-        org_emails = set()
-        org_members_fix = set()
-        org_emails_fix_name = set()
-        org_logins_fix_intel_email = set()
-
-        print(f'\nOrg members {org_members.totalCount} (login - name - company - email - valid):')
-        for org_member in org_members:
-            print_users(org_member)
-            if is_user_ignored(org_member):
-                continue
-            if is_intel_email(org_member.email):
-                org_emails.add(org_member.email.lower())
-                if not is_valid_name(org_member.name):
-                    org_members_fix.add(org_member)
-                    org_emails_fix_name.add(org_member.email.lower())
-            else:
-                org_members_fix.add(org_member)
-                org_logins_fix_intel_email.add(org_member.login.lower())
-
-        print_users(org_members_fix)
-        print(f'\nOrg members - no Intel emails {len(org_logins_fix_intel_email)}:',
-              '; '.join(org_logins_fix_intel_email))
-        print(f'\nOrg members - no real name {len(org_emails_fix_name)}:',
-              '; '.join(org_emails_fix_name))
-        return org_emails
-
-    def get_org_invitation_emails(self):
-        """Gets GitHub organization teams prints info"""
-        org_invitations = self.github_org.invitations()
-        org_invitation_emails = set()
-
-        print(f'\nOrg invitations {org_invitations.totalCount} (login - name - email - valid):')
-        for org_invitation in org_invitations:
-            # TODO: investigate GithubException while access to user name and enable print_users()
-            # github.GithubException.IncompletableObject: 400 "Returned object contains no URL"
-            #print_users(org_invitation)
-            print(f'{org_invitation.login} - ??? - {org_invitation.email} - ???')
-            if is_user_ignored(org_invitation):
-                continue
-            if is_intel_email(org_invitation.email):
-                org_invitation_emails.add(org_invitation.email.lower())
-            else:
-                print('Strange org invitation:', org_invitation)
-
-        print(f'\nOrg invitation emails {len(org_invitation_emails)}:',
-              '; '.join(org_invitation_emails))
-        return org_invitation_emails
-
-    def get_org_teams(self):
-        """Gets GitHub organization teams prints info"""
-        teams = []
-        org_teams = self.github_org.get_teams()
-        print('\nOrg teams count:', org_teams.totalCount)
-        for team in org_teams:
-            teams.append(team.name)
-            print(f'\nTeam: {team.name} - parent: {team.parent}')
-
-            repos = team.get_repos()
-            print('Repos:')
-            for repo in repos:
-                print(f'    {repo.name} -', team.get_repo_permission(repo))
-
-            team_maintainers = team.get_members(role='maintainer')
-            team_maintainer_logins = set()
-            for maintainer in team_maintainers:
-                team_maintainer_logins.add(maintainer.login)
-            team_members = team.get_members(role='member')
-            team_member_logins = set()
-            for member in team_members:
-                team_member_logins.add(member.login)
-            members = team.get_members(role='all')
-            member_emails = []
-            print('Members (role - login - name - company - email - valid):')
-            for user in members:
-                if user.login in team_maintainer_logins:
-                    print('    Maintainer - ', end='')
-                elif user.login in team_member_logins:
-                    print('    Member - ', end='')
-                else:
-                    # It is not possible to check child teams members
-                    print('    ??? - ', end='')
-                print_users(user)
-                if is_intel_email(user.email) and not is_user_ignored(user):
-                    member_emails.append(user.email.lower())
-            print(f'Intel emails {len(member_emails)}:', '; '.join(member_emails))
-        return teams
-
-    def get_valid_github_users(self, emails):
-        """Gets valid GitHub users by email and prints status"""
-        valid_users = set()
-        no_account_emails = set()
-        print(f'\nGitHub users from {len(emails)} invite emails (email - status):')
-        for email in emails:
-            if not is_intel_email(email):
-                print(f'{email} - Non Intel email')
-                continue
-
-            # You can make up to 30 requests per minute; https://developer.github.com/v3/search/
-            # Sleep 2.4 sec is about 25 requests per minute
-            time.sleep(2.4)
-            try:
-                users = self.github.search_users(f'{email} in:email')
-            except RateLimitExceededException:
-                time.sleep(5)
-                users = self.github.search_users(f'{email} in:email')
-
-            if users.totalCount == 0:
-                print(f'{email} - No valid GitHub account')
-                no_account_emails.add(email)
-                continue
-            if users.totalCount > 1:
-                print(f'{email} - Found {users.totalCount} GitHub accounts')
-            for user in users:
-                if user.email and user.email.lower() == email:
-                    print(f'{email} - OK')
-                    valid_users.add(user)
-                else:
-                    print(f'{email} - Non public or wrong email - login: {user.login} - '
-                          f'email: {user.email}')
-        print('Valid users count:', len(valid_users))
-        print_users(valid_users)
-        print(f'\nIntel emails - No valid GitHub account {len(no_account_emails)}:',
-              '; '.join(no_account_emails))
-        return valid_users
-
-    def invite_users(self, users):
-        """Invites users and prints status"""
-        if isinstance(users, (list, set)):
-            print(f'\nInvite {len(users)} users:')
-        else:
-            users = [users]
-
-        for user in users:
-            if isinstance(user, str):
-                print(f'Email: {user}')
-                self.github_org.invite_user(email=user)
-            else:
-                print(f'{user.login} - "{user.name}" - {user.email} - ', end='')
-                try:
-                    if is_user_ignored(user):
-                        print('Ignored')
-                        continue
-                    if not self._cfg.DRY_RUN:
-                        self.github_org.invite_user(user=user)
-                        print('OK')
-                    else:
-                        print('Dry run')
-                except GithubException as exc:
-                    print(f'FAIL: {exc.data["errors"][0]["message"]}')
-
-
-def _test():
-    """Test and debug"""
-    Config(cli_args=['DRY_RUN=True'])
-    dev_emails = get_dev_emails()
-    print('dev_emails:', dev_emails)
-
-    gh_api = GithubOrgApi()
-    gh_api.get_org_emails()
-
-
-if __name__ == '__main__':
-    _test()

.github/org_control/requirements.txt

@@ -1 +0,0 @@
-PyGithub==1.51

.github/org_control/requirements_dev.txt

@@ -1 +0,0 @@
-pylint==2.3.0

.github/workflows/code_style.yml

@@ -1,67 +0,0 @@
-name: Code Style
-on: [push, pull_request]
-
-jobs:
-  nGraph:
-    runs-on: ubuntu-18.04
-    steps:
-      - uses: actions/checkout@v2
-        with:
-          submodules: recursive
-
-      - name: Install clang-format-3.9
-        run: sudo apt --assume-yes install clang-format-3.9
-
-      - name: Install dependencies
-        run: |
-          sudo apt --assume-yes install libusb-1.0-0-dev
-          python3 -m pip install -r ./inference-engine/ie_bridges/python/requirements.txt
-
-      - name: CMake
-        run: |
-          mkdir build
-          cd build
-          cmake ..
-
-      - name: Check code style
-        run: make style-check
-        working-directory: build
-
-      - name: Create code style diff
-        if: failure()
-        run: |
-          ngraph/maint/apply-code-format.sh
-          git diff >ngraph_code_style_diff.patch
-
-      - uses: actions/upload-artifact@v2
-        if: failure()
-        with:
-          name: ngraph_code_style_diff
-          path: ngraph_code_style_diff.patch
-          
-  Java:
-    runs-on: ubuntu-18.04
-    steps:
-      - uses: actions/checkout@v2
-      - uses: actions/setup-java@v1
-        with:
-          java-version: '11'
-
-      - name: Install dependencies
-        run: |
-          wget -nc https://github.com/google/google-java-format/releases/download/google-java-format-1.9/google-java-format-1.9-all-deps.jar
-
-      - name: Check code style
-        run: |
-          java -jar google-java-format-1.9-all-deps.jar --set-exit-if-changed -a -i $(find . -type f -name "*.java")
-
-      - name: Create code style diff
-        if: failure()
-        run: |
-          git diff >java_code_style_diff.patch
-
-      - uses: actions/upload-artifact@v2
-        if: failure()
-        with:
-          name: java_code_style_diff
-          path: java_code_style_diff.patch

.github/workflows/mo.yml

@@ -32,6 +32,8 @@ jobs:
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip setuptools
+          # For Pylint
+          pip install tensorflow==1.14.0 tensorboard==1.14.0 tensorflow-estimator==1.14.0
           # For UT
           pip install unittest-xml-reporting==3.0.2
           # MO requirements

CMakeLists.txt

@@ -8,7 +8,17 @@ cmake_policy(SET CMP0054 NEW)
 # it allows to install targets created outside of current projects
 # See https://blog.kitware.com/cmake-3-13-0-available-for-download/
 
-cmake_minimum_required(VERSION 3.13 FATAL_ERROR)
+if (APPLE)
+    if(CMAKE_GENERATOR STREQUAL "Xcode")
+        # due to https://gitlab.kitware.com/cmake/cmake/issues/14254
+        cmake_minimum_required(VERSION 3.12.0 FATAL_ERROR)
+    else()
+        # due to https://cmake.org/cmake/help/v3.12/policy/CMP0068.html
+        cmake_minimum_required(VERSION 3.9 FATAL_ERROR)
+    endif()
+else()
+    cmake_minimum_required(VERSION 3.7.2 FATAL_ERROR)
+endif()
 
 project(OpenVINO)
 
@@ -56,16 +66,19 @@ function(build_ngraph)
         ngraph_set(NGRAPH_ADDRESS_SANITIZER FALSE)
     endif ()
     ngraph_set(NGRAPH_PYTHON_BUILD_ENABLE FALSE)
-
-    if(ENABLE_TESTS AND NOT ANDROID)
-        ngraph_set(NGRAPH_UNIT_TEST_ENABLE TRUE)
-    else()
-        ngraph_set(NGRAPH_UNIT_TEST_ENABLE FALSE)
-    endif()
-
-    if(NOT ANDROID)
+    if (NOT ANDROID)
+        if(ENABLE_TESTS)
+            ngraph_set(NGRAPH_UNIT_TEST_ENABLE TRUE)
+            ngraph_set(NGRAPH_IE_ENABLE TRUE)
+        else()
+            ngraph_set(NGRAPH_UNIT_TEST_ENABLE FALSE)
+            ngraph_set(NGRAPH_IE_ENABLE FALSE)
+        endif()
         ngraph_set(NGRAPH_ONNX_IMPORT_ENABLE TRUE)
     else()
+        ngraph_set(NGRAPH_UNIT_TEST_ENABLE FALSE)
+        ngraph_set(NGRAPH_TEST_UTIL_ENABLE FALSE)
+        ngraph_set(NGRAPH_IE_ENABLE FALSE)
         ngraph_set(NGRAPH_ONNX_IMPORT_ENABLE FALSE)
     endif()
     ngraph_set(NGRAPH_INTERPRETER_ENABLE TRUE)
@@ -85,13 +98,6 @@ function(build_ngraph)
     elseif(WIN32)
         set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /wd4308 /wd4146 /wd4703 /wd4244 /wd4819")
     endif()
-    
-    # Preserve the original flags for further use
-    set(CMAKE_ORIGINAL_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE}")
-    set(CMAKE_ORIGINAL_C_FLAGS_RELEASE "${CMAKE_C_FLAGS_RELEASE}")
-    set(CMAKE_ORIGINAL_EXE_LINKER_FLAGS_RELEASE "${CMAKE_EXE_LINKER_FLAGS_RELEASE}")
-    set(CMAKE_ORIGINAL_SHARED_LINKER_FLAGS_RELEASE "${CMAKE_SHARED_LINKER_FLAGS_RELEASE}")
-    set(CMAKE_ORIGINAL_MODULE_LINKER_FLAGS_RELEASE "${CMAKE_MODULE_LINKER_FLAGS_RELEASE}")
 
     if(ENABLE_LTO)
         ie_enable_lto()
@@ -105,34 +111,6 @@ function(build_ngraph)
     set(NGRAPH_LIBRARIES ngraph PARENT_SCOPE)
 endfunction()
 
-file(REMOVE "${CMAKE_BINARY_DIR}/openvino_targets_developer.cmake")
-
-unset(OpenVINODeveloperPackageTargets CACHE)
-
-function(openvino_developer_export_targets)
-    set(OpenVINODeveloperPackageTargets "${OpenVINODeveloperPackageTargets};${ARGV}")
-
-    # to allow exporting of aliased targets with the original names
-    foreach(target_name ${OpenVINODeveloperPackageTargets})
-        if(TARGET "${target_name}")
-            get_target_property(original_name ${target_name} ALIASED_TARGET)
-            if(TARGET "${original_name}")
-                message(STATUS "The name ${target_name} is an ALIAS for ${original_name}. "
-                        "It will be exported to the InferenceEngineDeveloperPackage with the original name.")
-                list(REMOVE_ITEM OpenVINODeveloperPackageTargets ${target_name})
-                list(APPEND OpenVINODeveloperPackageTargets ${original_name})
-            endif()
-        endif()
-    endforeach()
-
-    list(REMOVE_DUPLICATES OpenVINODeveloperPackageTargets)
-    set(OpenVINODeveloperPackageTargets "${OpenVINODeveloperPackageTargets}" CACHE INTERNAL
-        "Paths to extra Inference Engine plugins" FORCE)
-endfunction()
-
-
-add_subdirectory(openvino)
-
 build_ngraph()
 
 add_subdirectory(inference-engine)

CODEOWNERS

@@ -8,18 +8,13 @@ CODEOWNERS  @openvinotoolkit/openvino-admins @openvinotoolkit/openvino-maintaine
 Jenkinsfile  @openvinotoolkit/openvino-admins
 azure-pipelines.yml  @openvinotoolkit/openvino-admins
 /.github/  @openvinotoolkit/openvino-admins
-/.ci/  @openvinotoolkit/openvino-admins
 
 # QA Tests:
 /tests/  @openvinotoolkit/openvino-tests-maintainers
 
-# OpenVINO Scripts
-/scripts/ @openvinotoolkit/openvino-admins @openvinotoolkit/openvino-scripts-maintainers
-
 # IE Core:
 /inference-engine/  @openvinotoolkit/openvino-ie-maintainers
-/inference-engine/ie_bridges/python @openvinotoolkit/openvino-ie-python-api-maintainers
-/inference-engine/src/transformations/  @GlebKazantaev @ilyachur
+/inference-engine/src/transformations/  @GlebKazantaev @ichuraev
 /inference-engine/src/legacy_api/  @openvinotoolkit/openvino-ngraph-maintainers
 /inference-engine/src/readers/  @openvinotoolkit/openvino-ngraph-maintainers
 
@@ -69,7 +64,3 @@ azure-pipelines.yml  @openvinotoolkit/openvino-admins
 
 # Tools
 /tools/  @openvinotoolkit/openvino-tools-maintainers
-
-# Documentation
-/docs/  @openvinotoolkit/openvino-docs-maintainers
-*.md  @openvinotoolkit/openvino-docs-maintainers

CONTRIBUTING_DOCS.md

@@ -1,58 +0,0 @@
-# Contribute to Documentation
-
-If you want to contribute to a project documentation and make it better, your help is very welcome.
-This guide puts together the guidelines to help you figure out how you can offer your feedback and contribute to the documentation.
-
-## Contribute in Multiple ways
-
-There are multiple ways to help improve our documentation:
-* [Log an issue](https://jira.devtools.intel.com/projects/CVS/issues): Enter an issue for the OpenVINO™ documentation component for minor issues such as typos.
-* Make a suggestion: Send your documentation suggestion to the mailing list.
-* Contribute via GitHub: Submit pull requests in the [GitHub](https://github.com/openvinotoolkit/openvino/tree/master/docs) documentation repository.
-
-## Contribute via GitHub
-
-Use the following steps to contribute in the OpenVINO™ Toolkit documentation
-
-### Use Documentation Guidelines
-The documentation for our project is written using Markdown. Use our [guidelines](./docs/documentation_guidelines.md) and best practices to write consistent, readable documentation:
-
-* **[Authoring Guidelines](./docs/documentation_guidelines.md#authoring-guidelines)**
-* **[Structure Guidelines](./docs/documentation_guidelines.md#structure-guidelines)**
-* **[Formatting Guidelines](./docs/documentation_guidelines.md#structure-guidelines)**
-* **[Graphics Guidelines](./docs/documentation_guidelines.md#graphics-guidelines)**
-
-### Add New Document to the Documentation
-> **NOTE**: Please check if that information can be added to existing documents instead of creating a new one.
-
-1. Fork the [OpenVINO™ Toolkit](https://github.com/openvinotoolkit/openvino) repository.
-2. Create a new branch.
-3. Create a new markdown file in an appropriate folder.
-    > **REQUIRED**: The document title must contain a document label in a form: `{#openvino_docs_<name>}`. For example: `Deep Learning Network Intermediate Representation and Operation Sets in OpenVINO™ {#openvino_docs_MO_DG_IR_and_opsets}`.
-4. Add your file to the documentation structure. Open the documentation structure file [docs/doxygen/ie_docs.xml](./docs/doxygen/ie_docs.xml) and add your file path to the appropriate section.
-5. Commit changes to your branch.
-6. Create a pull request.
-7. Once the pull request is created, automatic checks are started. All checks must pass to continue.
-8. Discuss, review, and update your contributions.
-9. Get merged once the maintainer approves.
-
-### Edit Existing Document
-1. Fork the [OpenVINO™ Toolkit](https://github.com/openvinotoolkit/openvino) repository.
-2. Create a new branch.
-3. Edit the documentation markdown file and commit changes to the branch.
-4. Create a pull request.
-5. Once the pull request is created, automatic checks are started. All checks must pass to continue.
-6. Discuss, review, and update your contributions.
-7. Get merged once the maintainer approves.
-
-### Delete Document from the Documentation
-1. Fork the [OpenVINO™ Toolkit](https://github.com/openvinotoolkit/openvino) repository.
-2. Create a new branch.
-3. Remove the documentation file.
-4. Remove your file from the documentation structure. Open the documentation structure file [docs/doxygen/ie_docs.xml](./docs/doxygen/ie_docs.xml) and remove all occurences of your file path.
-5. Remove all references to that file from other documents or replace with links to alternatives topics (if any).
-6. Commit changes to your branch.
-7. Create a pull request.
-8. Once the pull request is created, automatic checks are started. All checks must pass to continue.
-9. Discuss, review, and update your contributions.
-10. Get merged once the maintainer approves.

Jenkinsfile

@@ -6,4 +6,5 @@ properties([
                      name: 'failFast')
     ])
 ])
+
 dldtPipelineEntrypoint(this)

README.md

@@ -1,16 +1,16 @@
 # [OpenVINO™ Toolkit](https://01.org/openvinotoolkit) - Deep Learning Deployment Toolkit repository
-[![Stable release](https://img.shields.io/badge/version-2021.1-green.svg)](https://github.com/openvinotoolkit/openvino/releases/tag/2021.1)
+[![Stable release](https://img.shields.io/badge/version-2020.4-green.svg)](https://github.com/openvinotoolkit/openvino/releases/tag/2020.4.0)
 [![Apache License Version 2.0](https://img.shields.io/badge/license-Apache_2.0-green.svg)](LICENSE)
 
-This toolkit allows developers to deploy pre-trained deep learning models
-through a high-level C++ Inference Engine API integrated with application logic.
+This toolkit allows developers to deploy pre-trained deep learning models 
+through a high-level C++ Inference Engine API integrated with application logic. 
 
-This open source version includes two components: namely [Model Optimizer] and
-[Inference Engine], as well as CPU, GPU and heterogeneous plugins to accelerate
-deep learning inferencing on Intel® CPUs and Intel® Processor Graphics.
-It supports pre-trained models from the [Open Model Zoo], along with 100+ open
-source and public models in popular formats such as Caffe\*, TensorFlow\*,
-MXNet\* and ONNX\*.
+This open source version includes two components: namely [Model Optimizer] and 
+[Inference Engine], as well as CPU, GPU and heterogeneous plugins to accelerate 
+deep learning inferencing on Intel® CPUs and Intel® Processor Graphics. 
+It supports pre-trained models from the [Open Model Zoo], along with 100+ open 
+source and public models in popular formats such as Caffe\*, TensorFlow\*, 
+MXNet\* and ONNX\*. 
 
 ## Repository components:
 * [Inference Engine]
@@ -18,7 +18,7 @@ MXNet\* and ONNX\*.
 
 ## License
 Deep Learning Deployment Toolkit is licensed under [Apache License Version 2.0](LICENSE).
-By contributing to the project, you agree to the license and copyright terms therein
+By contributing to the project, you agree to the license and copyright terms therein 
 and release your contribution under these terms.
 
 ## Documentation
@@ -30,15 +30,13 @@ and release your contribution under these terms.
 * [Model Optimizer Developer Guide](https://docs.openvinotoolkit.org/latest/_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html)
 
 ## How to Contribute
-See [CONTRIBUTING](./CONTRIBUTING.md) for contribution to the code.
-See [CONTRIBUTING_DOCS](./CONTRIBUTING_DOCS.md) for contribution to the documentation.
-Thank you!
+See [CONTRIBUTING](./CONTRIBUTING.md) for details. Thank you!
 
 ## Support
 Please report questions, issues and suggestions using:
 
 * The `openvino` [tag on StackOverflow]\*
-* [GitHub* Issues](https://github.com/openvinotoolkit/openvino/issues)
+* [GitHub* Issues](https://github.com/openvinotoolkit/openvino/issues) 
 * [Forum](https://software.intel.com/en-us/forums/computer-vision)
 
 ---

azure-pipelines.yml

@@ -0,0 +1,333 @@
+jobs:
+- job: Lin
+  # About 150% of total time
+  timeoutInMinutes: 75
+  pool:
+    #vmImage: 'ubuntu-18.04'
+    name: LIN_VMSS_VENV_F8S_WU2
+  variables:
+    BUILD_TYPE: Release
+    BIN_DIR: ../bin/intel64/$(BUILD_TYPE)
+  steps:
+  - script: |
+      whoami
+      uname -a
+      which python3
+      gcc --version
+      lsb_release
+      env
+      cat /proc/cpuinfo
+      cat /proc/meminfo
+      vmstat -s
+      df
+    displayName: 'System properties'
+  - script: |
+      sudo apt --assume-yes install libusb-1.0-0-dev
+      python3 -m pip install -r ./inference-engine/ie_bridges/python/requirements.txt
+      # For running Python API tests
+      python3 -m pip install -r ./inference-engine/ie_bridges/python/src/requirements-dev.txt
+    displayName: 'Install dependencies'
+  - script: |
+      wget https://github.com/ninja-build/ninja/releases/download/v1.10.0/ninja-linux.zip
+      unzip ninja-linux.zip
+      sudo cp -v ninja /usr/local/bin/
+    displayName: 'Install Ninja'
+  - script: git submodule update --init --recursive --jobs 8
+    displayName: 'Clone submodules'
+  - script: |
+      mkdir dldt-build
+      cd dldt-build
+    displayName: 'Create build directory'
+  - task: CMake@1
+    inputs:
+      workingDirectory: dldt-build
+      # CMake must get Python 3.x version by default
+      cmakeArgs: .. -GNinja -DVERBOSE_BUILD=ON -DCMAKE_BUILD_TYPE=$(BUILD_TYPE) -DENABLE_PYTHON=ON -DPYTHON_EXECUTABLE=/usr/bin/python3.6 -DENABLE_TESTS=ON
+  - script: ninja
+    workingDirectory: dldt-build
+    displayName: 'Build Lin'
+  - script: ls -alR ../bin/
+    workingDirectory: dldt-build
+    displayName: 'List files'
+  - script: $(BIN_DIR)/unit-test --gtest_print_time=1 --gtest_filter=-backend_api.config_unsupported:*IE_GPU*
+    workingDirectory: dldt-build
+    displayName: 'nGraph UT'
+    continueOnError: false
+  - script: $(BIN_DIR)/InferenceEngineUnitTests
+    workingDirectory: dldt-build
+    displayName: 'IE UT old'
+    continueOnError: false
+  - script: $(BIN_DIR)/ieUnitTests
+    workingDirectory: dldt-build
+    displayName: 'IE UT'
+    continueOnError: false
+  - script: $(BIN_DIR)/cpuUnitTests
+    workingDirectory: dldt-build
+    displayName: 'CPU UT'
+    continueOnError: false
+  - script: $(BIN_DIR)/gnaUnitTests
+    workingDirectory: dldt-build
+    displayName: 'GNA UT'
+    continueOnError: false
+  - script: $(BIN_DIR)/vpuUnitTests
+    workingDirectory: dldt-build
+    displayName: 'VPU UT'
+    continueOnError: false
+  - script: $(BIN_DIR)/ieFuncTests
+    workingDirectory: dldt-build
+    displayName: 'IE FuncTests'
+    continueOnError: false
+  - script: $(BIN_DIR)/cpuFuncTests
+    workingDirectory: dldt-build
+    displayName: 'CPU FuncTests'
+    continueOnError: false
+  - script: $(BIN_DIR)/MklDnnBehaviorTests
+    workingDirectory: dldt-build
+    displayName: 'MklDnnBehaviorTests'
+    continueOnError: false
+  - script: git clone https://github.com/openvinotoolkit/testdata.git
+    displayName: 'Clone testdata'
+  - script: |
+      export DATA_PATH=`pwd`/../testdata
+      export MODELS_PATH=`pwd`/../testdata
+      $(BIN_DIR)/MklDnnFunctionalTests --gtest_filter=*smoke*:-smoke_MobileNet/ModelTransformationsTest.LPT/mobilenet_v2_tf_depthwise_batch1_inPluginDisabled_inTestDisabled_asymmetric*
+    workingDirectory: dldt-build
+    displayName: 'MklDnnFunctionalTests'
+    continueOnError: false
+  - script: |
+      export DATA_PATH=`pwd`/../testdata
+      export MODELS_PATH=`pwd`/../testdata
+      $(BIN_DIR)/InferenceEngineCAPITests
+    workingDirectory: dldt-build
+    displayName: 'IE CAPITests'
+    continueOnError: false
+  - script: |
+      export DATA_PATH=`pwd`/../testdata
+      export MODELS_PATH=`pwd`/../testdata
+      export LD_LIBRARY_PATH=`pwd`/$(BIN_DIR)/lib
+      export PYTHONPATH=`pwd`/$(BIN_DIR)/lib/python_api/python3.6
+      env
+      cd ../inference-engine/ie_bridges/python/tests
+      pytest
+    workingDirectory: dldt-build
+    displayName: 'Python API Tests'
+    continueOnError: false
+    enabled: false
+
+- job: Mac
+  # About 200% of total time (perfomace of Mac hosts is unstable)
+  timeoutInMinutes: 180
+  pool:
+    vmImage: 'macOS-10.15'
+  variables:
+    BUILD_TYPE: Release
+    BIN_DIR: ../bin/intel64/$(BUILD_TYPE)
+  steps:
+  - task: UsePythonVersion@0
+    inputs:
+      versionSpec: '3.7'
+  - script: |
+      whoami
+      uname -a
+      which python3
+      gcc --version
+      xcrun --sdk macosx --show-sdk-version
+      env
+      sysctl -a
+    displayName: 'System properties'
+  - script: |
+      brew install cython
+      brew install automake
+    displayName: 'Install dependencies'
+  - script: brew install ninja
+    displayName: 'Install Ninja'
+  - script: git submodule update --init --recursive --jobs 8
+    displayName: 'Clone submodules'
+  - script: |
+      mkdir dldt-build
+      cd dldt-build
+    displayName: 'Create build directory'
+  - script: |
+      export PATH="/usr/local/opt/cython/bin:$PATH"
+      export CC=gcc
+      export CXX=g++
+      # Disable errors with Ninja
+      export CXXFLAGS="-Wno-error=unused-command-line-argument"
+      export CFLAGS="-Wno-error=unused-command-line-argument"
+      cmake .. -GNinja -DVERBOSE_BUILD=ON -DCMAKE_BUILD_TYPE=$(BUILD_TYPE) -DENABLE_PYTHON=ON -DENABLE_TESTS=ON
+    workingDirectory: dldt-build
+    displayName: 'CMake'
+  - script: ninja
+    workingDirectory: dldt-build
+    displayName: 'Build Mac'
+  - script: ls -alR ../bin/
+    workingDirectory: dldt-build
+    displayName: 'List files'
+  - script: $(BIN_DIR)/unit-test --gtest_print_time=1 --gtest_filter=-backend_api.config_unsupported:*IE_GPU*:IE_CPU.onnx_model_sigmoid
+    workingDirectory: dldt-build
+    displayName: 'nGraph UT'
+    continueOnError: false
+  - script: $(BIN_DIR)/InferenceEngineUnitTests
+    workingDirectory: dldt-build
+    displayName: 'IE UT old'
+    continueOnError: false
+  - script: $(BIN_DIR)/ieUnitTests
+    workingDirectory: dldt-build
+    displayName: 'IE UT'
+    continueOnError: false
+  - script: $(BIN_DIR)/cpuUnitTests
+    workingDirectory: dldt-build
+    displayName: 'CPU UT'
+    continueOnError: false
+  - script: $(BIN_DIR)/vpuUnitTests
+    workingDirectory: dldt-build
+    displayName: 'VPU UT'
+    continueOnError: false
+  - script: $(BIN_DIR)/ieFuncTests
+    workingDirectory: dldt-build
+    displayName: 'IE FuncTests'
+    continueOnError: false
+  - script: $(BIN_DIR)/cpuFuncTests
+    workingDirectory: dldt-build
+    displayName: 'CPU FuncTests'
+    continueOnError: false
+  - script: $(BIN_DIR)/MklDnnBehaviorTests
+    workingDirectory: dldt-build
+    displayName: 'MklDnnBehaviorTests'
+    continueOnError: false
+  - script: git clone https://github.com/openvinotoolkit/testdata.git
+    displayName: 'Clone testdata'
+  - script: |
+      export DATA_PATH=`pwd`/../testdata
+      export MODELS_PATH=`pwd`/../testdata
+      $(BIN_DIR)/MklDnnFunctionalTests --gtest_filter=*smoke*:-smoke_MobileNet/ModelTransformationsTest.LPT/mobilenet_v2_tf_depthwise_batch1_inPluginDisabled_inTestDisabled_asymmetric*
+    workingDirectory: dldt-build
+    displayName: 'MklDnnFunctionalTests'
+    continueOnError: false
+  - script: |
+      export DATA_PATH=`pwd`/../testdata
+      export MODELS_PATH=`pwd`/../testdata
+      $(BIN_DIR)/InferenceEngineCAPITests
+    workingDirectory: dldt-build
+    displayName: 'IE CAPITests'
+    continueOnError: false
+
+- job: Win
+  # About 150% of total time
+  timeoutInMinutes: 120
+  pool:
+    #vmImage: 'vs2017-win2016'
+    name: WIN_VMSS_VENV_F8S_WU2
+  variables:
+    BUILD_TYPE: Release
+    BUILD_DIR: D:\dldt-build
+    BIN_DIR: ..\bin\intel64
+    MSVS_VARS_PATH: C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise\VC\Auxiliary\Build\vcvars64.bat
+    MSVC_COMPILER_PATH: C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise\VC\Tools\MSVC\14.24.28314\bin\Hostx64\x64\cl.exe
+  steps:
+  - script: |
+      where python3
+      wmic computersystem get TotalPhysicalMemory
+      wmic cpu list
+      wmic logicaldisk get description,name
+      wmic VOLUME list
+      set
+    displayName: 'System properties'
+  - script: |
+      certutil -urlcache -split -f https://github.com/ninja-build/ninja/releases/download/v1.10.0/ninja-win.zip ninja-win.zip
+      powershell -command "Expand-Archive -Force ninja-win.zip"
+    displayName: Install Ninja
+  - script: git submodule update --init --recursive --jobs 8
+    displayName: 'Clone submodules'
+  - script: |
+      rd /Q /S $(BUILD_DIR)
+      mkdir $(BUILD_DIR)\bin
+      rd /Q /S dldt-build
+      mkdir dldt-build
+    displayName: 'Create build directory'
+  - script: |
+      set PATH=$(Build.Repository.LocalPath)\ninja-win;%PATH%
+      call "$(MSVS_VARS_PATH)" && cmake -GNinja -DCMAKE_BUILD_TYPE=$(BUILD_TYPE) -DENABLE_TESTS=ON -DCMAKE_C_COMPILER:PATH="$(MSVC_COMPILER_PATH)" -DCMAKE_CXX_COMPILER:PATH="$(MSVC_COMPILER_PATH)" $(Build.Repository.LocalPath)
+    workingDirectory: $(BUILD_DIR)
+    displayName: 'CMake'
+  - script: |
+      set PATH=$(Build.Repository.LocalPath)\ninja-win;%PATH%
+      call "$(MSVS_VARS_PATH)" && ninja
+    workingDirectory: $(BUILD_DIR)
+    displayName: 'Build Win'
+  - script: dir ..\bin\ /s /b
+    workingDirectory: dldt-build
+    displayName: 'List files'
+  - script: |
+      set PATH=$(Build.Repository.LocalPath)\inference-engine\temp\tbb\bin;%PATH%
+      $(BIN_DIR)\unit-test --gtest_print_time=1 --gtest_filter=-backend_api.config_unsupported:*IE_GPU*
+    workingDirectory: dldt-build
+    displayName: 'nGraph UT'
+    continueOnError: false
+  - script: |
+      set PATH=$(Build.Repository.LocalPath)\inference-engine\temp\tbb\bin;%PATH%
+      $(BIN_DIR)\InferenceEngineUnitTests
+    workingDirectory: dldt-build
+    displayName: 'IE UT old'
+    continueOnError: false
+  - script: |
+      set PATH=$(Build.Repository.LocalPath)\inference-engine\temp\tbb\bin;%PATH%
+      $(BIN_DIR)\ieUnitTests
+    workingDirectory: dldt-build
+    displayName: 'IE UT'
+    continueOnError: false
+  - script: |
+      set PATH=$(Build.Repository.LocalPath)\inference-engine\temp\tbb\bin;%PATH%
+      $(BIN_DIR)\cpuUnitTests
+    workingDirectory: dldt-build
+    displayName: 'CPU UT'
+    continueOnError: false
+  - script: |
+      set PATH=$(Build.Repository.LocalPath)\inference-engine\temp\tbb\bin;%PATH%
+      $(BIN_DIR)\gnaUnitTests
+    workingDirectory: dldt-build
+    displayName: 'GNA UT'
+    continueOnError: false
+  - script: |
+      set PATH=$(Build.Repository.LocalPath)\inference-engine\temp\tbb\bin;%PATH%
+      $(BIN_DIR)\vpuUnitTests
+    workingDirectory: dldt-build
+    displayName: 'VPU UT'
+    continueOnError: false
+  - script: |
+      set PATH=$(Build.Repository.LocalPath)\inference-engine\temp\tbb\bin;%PATH%
+      $(BIN_DIR)\ieFuncTests
+    workingDirectory: dldt-build
+    displayName: 'IE FuncTests'
+    continueOnError: false
+  - script: |
+      set PATH=$(Build.Repository.LocalPath)\inference-engine\temp\tbb\bin;%PATH%
+      $(BIN_DIR)\cpuFuncTests
+    workingDirectory: dldt-build
+    displayName: 'CPU FuncTests'
+    continueOnError: false
+  - script: |
+      set PATH=$(Build.Repository.LocalPath)\inference-engine\temp\tbb\bin;%PATH%
+      $(BIN_DIR)\MklDnnBehaviorTests
+    workingDirectory: dldt-build
+    displayName: 'MklDnnBehaviorTests'
+    continueOnError: false
+  - script: git clone https://github.com/openvinotoolkit/testdata.git
+    workingDirectory: $(BUILD_DIR)
+    displayName: 'Clone testdata'
+  - script: |
+      set PATH=$(Build.Repository.LocalPath)\inference-engine\temp\tbb\bin;$(Build.Repository.LocalPath)\inference-engine\temp\opencv_4.3.0\opencv\bin;%PATH%
+      set DATA_PATH=$(BUILD_DIR)\testdata
+      set MODELS_PATH=$(BUILD_DIR)\testdata
+      $(BIN_DIR)\MklDnnFunctionalTests --gtest_filter=*smoke*:-smoke_MobileNet/ModelTransformationsTest.LPT/mobilenet_v2_tf_depthwise_batch1_inPluginDisabled_inTestDisabled_asymmetric*
+    workingDirectory: dldt-build
+    displayName: 'MklDnnFunctionalTests'
+    continueOnError: false
+  - script: |
+      set PATH=$(Build.Repository.LocalPath)\inference-engine\temp\tbb\bin;$(Build.Repository.LocalPath)\inference-engine\temp\opencv_4.3.0\opencv\bin;%PATH%
+      set DATA_PATH=$(BUILD_DIR)\testdata
+      set MODELS_PATH=$(BUILD_DIR)\testdata
+      $(BIN_DIR)\InferenceEngineCAPITests
+    workingDirectory: dldt-build
+    displayName: 'IE CAPITests'
+    continueOnError: false

build-instruction.md

@@ -46,19 +46,21 @@ The open source version of Inference Engine includes the following plugins:
 | MYRIAD plugin        | Intel® Movidius™ Neural Compute Stick powered by the Intel® Movidius™ Myriad™ 2, Intel® Neural Compute Stick 2 powered by the Intel® Movidius™ Myriad™ X |
 | Heterogeneous plugin | Heterogeneous plugin enables computing for inference on one network on several Intel® devices. |
 
+Inference Engine plugin for Intel® FPGA is distributed only in a binary form,
+as a part of [Intel® Distribution of OpenVINO™].
+
 ## Build on Linux\* Systems
 
 The software was validated on:
 - Ubuntu\* 18.04 (64-bit) with default GCC\* 7.5.0
-- Ubuntu\* 20.04 (64-bit) with default GCC\* 9.3.0
-- CentOS\* 7.6 (64-bit) with default GCC\* 4.8.5
+- Ubuntu\* 16.04 (64-bit) with default GCC\* 5.4.0
+- CentOS\* 7.4 (64-bit) with default GCC\* 4.8.5
 
 ### Software Requirements
-- [CMake]\* 3.13 or higher
+- [CMake]\* 3.11 or higher
 - GCC\* 4.8 or higher to build the Inference Engine
-- Python 3.6 or higher for Inference Engine Python API wrapper
+- Python 3.5 or higher for Inference Engine Python API wrapper
 - (Optional) [Install Intel® Graphics Compute Runtime for OpenCL™ Driver package 19.41.14441].
-> **NOTE**: Building samples and demos from the Intel® Distribution of OpenVINO™ toolkit package requires CMake\* 3.10 or higher. 
 
 ### Build Steps
 1. Clone submodules:
@@ -66,13 +68,13 @@ The software was validated on:
     cd openvino
     git submodule update --init --recursive
     ```
-2. Install build dependencies using the `install_build_dependencies.sh` script in the
+2. Install build dependencies using the `install_dependencies.sh` script in the
    project root folder.
    ```sh
-   chmod +x install_build_dependencies.sh
+   chmod +x install_dependencies.sh
    ```
    ```sh
-   ./install_build_dependencies.sh
+   ./install_dependencies.sh
    ```
 3. By default, the build enables the Inference Engine GPU plugin to infer models
    on your Intel® Processor Graphics. This requires you to
@@ -144,6 +146,7 @@ You can use the following additional build options:
 
 - nGraph-specific compilation options:
   `-DNGRAPH_ONNX_IMPORT_ENABLE=ON` enables the building of the nGraph ONNX importer.
+  `-DNGRAPH_JSON_ENABLE=ON` enables nGraph JSON-based serialization.
   `-DNGRAPH_DEBUG_ENABLE=ON` enables additional debug prints.
 
 ## Build for Raspbian Stretch* OS
@@ -240,9 +243,7 @@ with the following content:
       libgstreamer1.0-dev:armhf \
       libgstreamer-plugins-base1.0-dev:armhf \
       libpython3-dev:armhf \
-      python3-pip \
-      python-minimal \
-      python-argparse
+      python3-pip
 
   RUN wget https://www.cmake.org/files/v3.14/cmake-3.14.3.tar.gz && \
       tar xf cmake-3.14.3.tar.gz && \
@@ -324,19 +325,20 @@ You can use the following additional build options:
 
 - nGraph-specific compilation options:
   `-DNGRAPH_ONNX_IMPORT_ENABLE=ON` enables the building of the nGraph ONNX importer.
+  `-DNGRAPH_JSON_ENABLE=ON` enables nGraph JSON-based serialization.
   `-DNGRAPH_DEBUG_ENABLE=ON` enables additional debug prints.
 
 ## Build on Windows* Systems
 
 The software was validated on:
-- Microsoft\* Windows\* 10 (64-bit) with Visual Studio 2019
+- Microsoft\* Windows\* 10 (64-bit) with Visual Studio 2017 and Intel® C++
+  Compiler 2018 Update 3
 
 ### Software Requirements
-- [CMake]\*3.13 or higher
-- Microsoft\* Visual Studio 2017, 2019
+- [CMake]\*3.11 or higher
+- Microsoft\* Visual Studio 2017, 2019 or [Intel® C++ Compiler] 18.0
 - (Optional) Intel® Graphics Driver for Windows* (26.20) [driver package].
-- Python 3.6 or higher for Inference Engine Python API wrapper
-> **NOTE**: Building samples and demos from the Intel® Distribution of OpenVINO™ toolkit package requires CMake\* 3.10 or higher. 
+- Python 3.5 or higher for Inference Engine Python API wrapper
 
 ### Build Steps
 
@@ -367,13 +369,20 @@ cmake -G "Visual Studio 15 2017 Win64" -DCMAKE_BUILD_TYPE=Release ..
 cmake -G "Visual Studio 16 2019" -A x64 -DCMAKE_BUILD_TYPE=Release ..
 ```
 
+   For Intel® C++ Compiler 18:
+```sh
+cmake -G "Visual Studio 15 2017 Win64" -T "Intel C++ Compiler 18.0" ^
+    -DCMAKE_BUILD_TYPE=Release ^
+    -DICCLIB="C:\Program Files (x86)\IntelSWTools\compilers_and_libraries_2018\windows\compiler\lib" ..
+```
+
 5. Build generated solution in Visual Studio or run
    `cmake --build . --config Release` to build from the command line.
 
 6. Before running the samples, add paths to the TBB and OpenCV binaries used for
    the build to the `%PATH%` environment variable. By default, TBB binaries are
    downloaded by the CMake-based script to the `<openvino_repo>/inference-engine/temp/tbb/bin`
-   folder, OpenCV binaries to the `<openvino_repo>/inference-engine/temp/opencv_4.5.0/opencv/bin`
+   folder, OpenCV binaries to the `<openvino_repo>/inference-engine/temp/opencv_4.3.0/opencv/bin`
    folder.
 
 ### Additional Build Options
@@ -419,6 +428,7 @@ cmake -G "Visual Studio 16 2019" -A x64 -DCMAKE_BUILD_TYPE=Release ..
 
 - nGraph-specific compilation options:
   `-DNGRAPH_ONNX_IMPORT_ENABLE=ON` enables the building of the nGraph ONNX importer.
+  `-DNGRAPH_JSON_ENABLE=ON` enables nGraph JSON-based serialization.
   `-DNGRAPH_DEBUG_ENABLE=ON` enables additional debug prints.
 
 ### Building Inference Engine with Ninja* Build System
@@ -439,14 +449,13 @@ cmake --build . --config Release
 inference on Intel CPUs only.
 
 The software was validated on:
-- macOS\* 10.15, 64-bit
+- macOS\* 10.14, 64-bit
 
 ### Software Requirements
 
-- [CMake]\* 3.13 or higher
+- [CMake]\* 3.11 or higher
 - Clang\* compiler from Xcode\* 10.1 or higher
-- Python\* 3.6 or higher for the Inference Engine Python API wrapper
-> **NOTE**: Building samples and demos from the Intel® Distribution of OpenVINO™ toolkit package requires CMake\* 3.10 or higher. 
+- Python\* 3.5 or higher for the Inference Engine Python API wrapper
 
 ### Build Steps
 
@@ -455,11 +464,19 @@ The software was validated on:
     cd openvino
     git submodule update --init --recursive
     ```
-2. Create a build folder:
+2. Install build dependencies using the `install_dependencies.sh` script in the
+   project root folder:
+   ```sh
+   chmod +x install_dependencies.sh
+   ```
+   ```sh
+   ./install_dependencies.sh
+   ```
+3. Create a build folder:
 ```sh
-  mkdir build && cd build
+  mkdir build
 ```
-3. Inference Engine uses a CMake-based build system. In the created `build`
+4. Inference Engine uses a CMake-based build system. In the created `build`
    directory, run `cmake` to fetch project dependencies and create Unix makefiles,
    then run `make` to build the project:
 ```sh
@@ -495,19 +512,15 @@ You can use the following additional build options:
 
 - To build the Python API wrapper, use the `-DENABLE_PYTHON=ON` option. To
   specify an exact Python version, use the following options:
-   - If you installed Python through Homebrew*, set the following flags:
-   ```sh
-   -DPYTHON_EXECUTABLE=/usr/local/Cellar/python/3.7.7/Frameworks/Python.framework/Versions/3.7/bin/python3.7m \
-   -DPYTHON_LIBRARY=/usr/local/Cellar/python/3.7.7/Frameworks/Python.framework/Versions/3.7/lib/libpython3.7m.dylib \
-   -DPYTHON_INCLUDE_DIR=/usr/local/Cellar/python/3.7.7/Frameworks/Python.framework/Versions/3.7/include/python3.7m 
-   ```
-   - If you installed Python another way, you can use the following commands to find where the `dylib` and `include_dir` are located, respectively: 
-   ```sh
-   find /usr/ -name 'libpython*m.dylib'
-   find /usr/ -type d -name python3.7m
-   ```
+```sh
+  -DPYTHON_EXECUTABLE=/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 \
+  -DPYTHON_LIBRARY=/Library/Frameworks/Python.framework/Versions/3.7/lib/libpython3.7m.dylib \
+  -DPYTHON_INCLUDE_DIR=/Library/Frameworks/Python.framework/Versions/3.7/include/python3.7m
+```
+
 - nGraph-specific compilation options:
   `-DNGRAPH_ONNX_IMPORT_ENABLE=ON` enables the building of the nGraph ONNX importer.
+  `-DNGRAPH_JSON_ENABLE=ON` enables nGraph JSON-based serialization.
   `-DNGRAPH_DEBUG_ENABLE=ON` enables additional debug prints.
 
 ## Build on Android* Systems
@@ -516,9 +529,8 @@ This section describes how to build Inference Engine for Android x86 (64-bit) op
 
 ### Software Requirements
 
-- [CMake]\* 3.13 or higher
+- [CMake]\* 3.11 or higher
 - Android NDK (this guide has been validated with r20 release)
-> **NOTE**: Building samples and demos from the Intel® Distribution of OpenVINO™ toolkit package requires CMake\* 3.10 or higher. 
 
 ### Build Steps
 
@@ -688,4 +700,5 @@ This target collects all dependencies, prepares the nGraph package and copies it
 [build instructions]:https://docs.opencv.org/master/df/d65/tutorial_table_of_content_introduction.html
 [driver package]:https://downloadcenter.intel.com/download/29335/Intel-Graphics-Windows-10-DCH-Drivers
 [Intel® Neural Compute Stick 2 Get Started]:https://software.intel.com/en-us/neural-compute-stick/get-started
+[Intel® C++ Compiler]:https://software.intel.com/en-us/intel-parallel-studio-xe
 [OpenBLAS]:https://sourceforge.net/projects/openblas/files/v0.2.14/OpenBLAS-v0.2.14-Win64-int64.zip/download

cmake/developer_package.cmake

@@ -122,9 +122,9 @@ include(debug)
 include(whole_archive)
 
 string(TOLOWER ${CMAKE_SYSTEM_PROCESSOR} ARCH_FOLDER)
-if(X86_64)
+if(ARCH_FOLDER STREQUAL "x86_64" OR ARCH_FOLDER STREQUAL "amd64") # Windows detects Intel's 64-bit CPU as AMD64
     set(ARCH_FOLDER intel64)
-elseif(X86)
+elseif(ARCH_FOLDER STREQUAL "i386")
     set(ARCH_FOLDER ia32)
 endif()
 

cmake/features.cmake

@@ -28,8 +28,6 @@ ie_option (OS_FOLDER "create OS dedicated folder in output" OFF)
 # FIXME: ARM cross-compiler generates several "false positive" warnings regarding __builtin_memcpy buffer overflow
 ie_dependent_option (TREAT_WARNING_AS_ERROR "Treat build warnings as errors" ON "X86 OR X86_64" OFF)
 
-ie_option (ENABLE_INTEGRITYCHECK "build DLLs with /INTEGRITYCHECK flag" OFF)
-
 ie_option (ENABLE_SANITIZER "enable checking memory errors via AddressSanitizer" OFF)
 
 ie_option (ENABLE_THREAD_SANITIZER "enable checking data races via ThreadSanitizer" OFF)
@@ -43,8 +41,3 @@ ie_dependent_option (ENABLE_SSE42 "Enable SSE4.2 optimizations" ON "X86_64 OR X8
 ie_dependent_option (ENABLE_AVX2 "Enable AVX2 optimizations" ON "X86_64 OR X86" OFF)
 
 ie_dependent_option (ENABLE_AVX512F "Enable AVX512 optimizations" ON "X86_64 OR X86" OFF)
-
-ie_dependent_option (ENABLE_PROFILING_ITT "ITT tracing of IE and plugins internals" ON "NOT CMAKE_CROSSCOMPILING" OFF)
-
-# Documentation build
-ie_option (ENABLE_DOCS "build docs using Doxygen" OFF)

cmake/os_flags.cmake

@@ -238,17 +238,16 @@ if(WIN32)
     endif()
 
     if(CMAKE_CXX_COMPILER_ID STREQUAL "Intel")
-        # 161: unrecognized pragma
-        # 177: variable was declared but never referenced
-        # 556: not matched type of assigned function pointer
+        # 161 unrecognized pragma
+        # 177 variable was declared but never referenced
+        # 556 not matched type of assigned function pointer
         # 1744: field of class type without a DLL interface used in a class with a DLL interface
-        # 1879: unimplemented pragma ignored
-        # 2586: decorated name length exceeded, name was truncated
+        # 2586 decorated name length exceeded, name was truncated
         # 2651: attribute does not apply to any entity
-        # 3180: unrecognized OpenMP pragma
+        # 3180 unrecognized OpenMP pragma
         # 11075: To get full report use -Qopt-report:4 -Qopt-report-phase ipo
-        # 15335: was not vectorized: vectorization possible but seems inefficient. Use vector always directive or /Qvec-threshold0 to override
-        ie_add_compiler_flags(/Qdiag-disable:161,177,556,1744,1879,2586,2651,3180,11075,15335)
+        # 15335 was not vectorized: vectorization possible but seems inefficient. Use vector always directive or /Qvec-threshold0 to override
+        ie_add_compiler_flags(/Qdiag-disable:161,177,556,1744,2586,2651,3180,11075,15335)
     endif()
 
     # Debug information flags
@@ -265,7 +264,6 @@ else()
     ie_add_compiler_flags(-ffunction-sections -fdata-sections)
     ie_add_compiler_flags(-fdiagnostics-show-option)
     ie_add_compiler_flags(-Wundef)
-    ie_add_compiler_flags(-Wreturn-type)
 
     # Disable noisy warnings
 

cmake/sanitizer.cmake

@@ -15,9 +15,7 @@ if (ENABLE_SANITIZER)
     if(CMAKE_CXX_COMPILER_ID STREQUAL "GNU")
         set(SANITIZER_LINKER_FLAGS "${SANITIZER_LINKER_FLAGS} -fuse-ld=gold")
     elseif(CMAKE_CXX_COMPILER_ID MATCHES "^(Apple)?Clang$" AND NOT WIN32)
-        if(CMAKE_CXX_COMPILER_VERSION VERSION_GREATER_EQUAL 8.0)
-            set(SANITIZER_LINKER_FLAGS "${SANITIZER_LINKER_FLAGS} -fuse-ld=lld")
-        endif()
+        set(SANITIZER_LINKER_FLAGS "${SANITIZER_LINKER_FLAGS} -fuse-ld=lld")
     endif()
 
     set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${SANITIZER_COMPILER_FLAGS}")

cmake/sdl.cmake

@@ -14,7 +14,9 @@ if (CMAKE_BUILD_TYPE STREQUAL "Release")
         endif()
 
         if(CMAKE_CXX_COMPILER_ID STREQUAL "GNU")
-            set(IE_LINKER_FLAGS "${IE_LINKER_FLAGS} -z noexecstack -z relro -z now")
+            set(CMAKE_SHARED_LINKER_FLAGS_RELEASE "${CMAKE_SHARED_LINKER_FLAGS_RELEASE} -z noexecstack -z relro -z now")
+            set(CMAKE_MODULE_LINKER_FLAGS_RELEASE "${CMAKE_MODULE_LINKER_FLAGS_RELEASE} -z noexecstack -z relro -z now")
+            set(CMAKE_EXE_LINKER_FLAGS_RELEASE "${CMAKE_EXE_LINKER_FLAGS_RELEASE} -z noexecstack -z relro -z now")
             if(CMAKE_CXX_COMPILER_VERSION VERSION_LESS 4.9)
                 set(IE_C_CXX_FLAGS "${IE_C_CXX_FLAGS} -fstack-protector-all")
             else()
@@ -30,21 +32,14 @@ if (CMAKE_BUILD_TYPE STREQUAL "Release")
                 set(IE_C_CXX_FLAGS "${IE_C_CXX_FLAGS} -Wl,--strip-all")
             endif()
             set(IE_C_CXX_FLAGS "${IE_C_CXX_FLAGS} -fstack-protector-strong")
-            set(IE_LINKER_FLAGS "${IE_LINKER_FLAGS} -z noexecstack -z relro -z now")
-        endif()
-    else()
-        if(CMAKE_CXX_COMPILER_ID STREQUAL "MSVC")
-            set(IE_C_CXX_FLAGS "${IE_C_CXX_FLAGS} /sdl")
-        endif()
-        set(IE_C_CXX_FLAGS "${IE_C_CXX_FLAGS} /guard:cf")
-        if(ENABLE_INTEGRITYCHECK)
-            set(CMAKE_SHARED_LINKER_FLAGS_RELEASE "${CMAKE_SHARED_LINKER_FLAGS_RELEASE} /INTEGRITYCHECK")
+            set(CMAKE_SHARED_LINKER_FLAGS_RELEASE "${CMAKE_SHARED_LINKER_FLAGS_RELEASE} -z noexecstack -z relro -z now")
+            set(CMAKE_MODULE_LINKER_FLAGS_RELEASE "${CMAKE_MODULE_LINKER_FLAGS_RELEASE} -z noexecstack -z relro -z now")
+            set(CMAKE_EXE_LINKER_FLAGS_RELEASE "${CMAKE_EXE_LINKER_FLAGS_RELEASE} -z noexecstack -z relro -z now")
         endif()
+    elseif(CMAKE_CXX_COMPILER_ID STREQUAL "MSVC")
+        set(IE_C_CXX_FLAGS "${IE_C_CXX_FLAGS} /sdl /guard:cf")
     endif()
 
     set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${IE_C_CXX_FLAGS}")
     set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${IE_C_CXX_FLAGS}")
-    set(CMAKE_SHARED_LINKER_FLAGS_RELEASE "${CMAKE_SHARED_LINKER_FLAGS_RELEASE} ${IE_LINKER_FLAGS}")
-    set(CMAKE_MODULE_LINKER_FLAGS_RELEASE "${CMAKE_MODULE_LINKER_FLAGS_RELEASE} ${IE_LINKER_FLAGS}")
-    set(CMAKE_EXE_LINKER_FLAGS_RELEASE "${CMAKE_EXE_LINKER_FLAGS_RELEASE} ${IE_LINKER_FLAGS}")
 endif()

cmake/target_flags.cmake

@@ -30,13 +30,6 @@ elseif(CMAKE_SYSTEM_PROCESSOR MATCHES "^(aarch64.*|AARCH64.*)")
   set(AARCH64 ON)
 endif()
 
-# in case of cross-compilation (or -m32) CMAKE_SYSTEM_PROCESSOR is equal to
-# CMAKE_HOST_SYSTEM_PROCESSOR which is X86_64; patch this until a better solution
-if(CMAKE_SIZEOF_VOID_P EQUAL 4 AND X86_64)
-  unset(X86_64)
-  set(X86 ON)
-endif()
-
 if(UNIX AND NOT APPLE)
     set(LINUX ON)
 endif()

docs/CMakeLists.txt

@@ -2,187 +2,59 @@
 # SPDX-License-Identifier: Apache-2.0
 #
 
-if(NOT ENABLE_DOCKER)
-    add_subdirectory(examples)
+add_subdirectory(examples)
 
-    # Detect nGraph
-    find_package(ngraph QUIET)
-    if(NOT ngraph_FOUND)
-        set(ngraph_DIR ${CMAKE_BINARY_DIR}/ngraph)
-    endif()
-
-    # Detect InferenceEngine
-    find_package(InferenceEngine QUIET)
-    if(NOT InferenceEngine_FOUND)
-        set(InferenceEngine_DIR ${CMAKE_BINARY_DIR})
-    endif()
-
-    add_subdirectory(template_extension)
-
-    set(all_docs_targets
-        ie_docs_examples
-        template_extension
-        templatePlugin TemplateBehaviorTests TemplateFunctionalTests)
-    foreach(target_name IN LISTS all_docs_targets)
-        if (TARGET ${target_name})
-            set_target_properties(${target_name} PROPERTIES FOLDER docs)
-        endif()
-    endforeach()
+# Detect nGraph
+find_package(ngraph QUIET)
+if(NOT ngraph_FOUND)
+    set(ngraph_DIR ${CMAKE_BINARY_DIR}/ngraph)
 endif()
 
-function(build_docs)
-    find_package(Doxygen REQUIRED dot)
-    find_package(Python3 COMPONENTS Interpreter)
-    find_package(LATEX)
-
-    if(NOT DOXYGEN_FOUND)
-        message(FATAL_ERROR "Doxygen is required to build the documentation")
-    endif()
-
-    if(NOT Python3_FOUND)
-        message(FATAL_ERROR "Python3 is required to build the documentation")
-    endif()
-
-    if(NOT LATEX_FOUND)
-        message(FATAL_ERROR "LATEX is required to build the documentation")
-    endif()
-
-    set(DOCS_BINARY_DIR "${CMAKE_CURRENT_BINARY_DIR}")
-    set(DOXYGEN_DIR "${OpenVINO_MAIN_SOURCE_DIR}/docs/doxygen")
-    set(IE_SOURCE_DIR "${OpenVINO_MAIN_SOURCE_DIR}/inference-engine")
-    set(PYTHON_API_IN "${IE_SOURCE_DIR}/ie_bridges/python/src/openvino/inference_engine/ie_api.pyx")
-    set(PYTHON_API_OUT "${DOCS_BINARY_DIR}/python_api/ie_api.pyx")
-    set(C_API "${IE_SOURCE_DIR}/ie_bridges/c/include")
-    set(PLUGIN_API_DIR "${DOCS_BINARY_DIR}/IE_PLUGIN_DG")
-
-    # Preprocessing scripts
-    set(DOXY_MD_FILTER "${DOXYGEN_DIR}/doxy_md_filter.py")
-    set(PYX_FILTER "${DOXYGEN_DIR}/pyx_filter.py")
-
-    file(GLOB_RECURSE doc_source_files
-        LIST_DIRECTORIES true RELATIVE ${OpenVINO_MAIN_SOURCE_DIR}
-        "${OpenVINO_MAIN_SOURCE_DIR}/docs/*.md" 
-        "${OpenVINO_MAIN_SOURCE_DIR}/docs/*.png" 
-        "${OpenVINO_MAIN_SOURCE_DIR}/docs/*.gif" 
-        "${OpenVINO_MAIN_SOURCE_DIR}/docs/*.jpg" 
-        "${OpenVINO_MAIN_SOURCE_DIR}/inference-engine/*.md"
-        "${OpenVINO_MAIN_SOURCE_DIR}/inference-engine/*.png"
-        "${OpenVINO_MAIN_SOURCE_DIR}/inference-engine/*.gif"
-        "${OpenVINO_MAIN_SOURCE_DIR}/inference-engine/*.jpg")
-
-    configure_file(${PYTHON_API_IN} ${PYTHON_API_OUT} @ONLY)
-
-    set(IE_CONFIG_SOURCE "${DOXYGEN_DIR}/ie_docs.config")
-    set(C_CONFIG_SOURCE "${DOXYGEN_DIR}/ie_c_api.config")
-    set(PY_CONFIG_SOURCE "${DOXYGEN_DIR}/ie_py_api.config")
-    set(PLUGIN_CONFIG_SOURCE "${DOXYGEN_DIR}/ie_plugin_api.config")
-
-    set(IE_CONFIG_BINARY "${DOCS_BINARY_DIR}/ie_docs.config")
-    set(C_CONFIG_BINARY "${DOCS_BINARY_DIR}/ie_c_api.config")
-    set(PY_CONFIG_BINARY "${DOCS_BINARY_DIR}/ie_py_api.config")
-    set(PLUGIN_CONFIG_BINARY "${DOCS_BINARY_DIR}/ie_plugin_api.config")
-
-    set(IE_LAYOUT_SOURCE "${DOXYGEN_DIR}/ie_docs.xml")
-    set(C_LAYOUT_SOURCE "${DOXYGEN_DIR}/ie_c_api.xml")
-    set(PY_LAYOUT_SOURCE "${DOXYGEN_DIR}/ie_py_api.xml")
-    set(PLUGIN_LAYOUT_SOURCE "${DOXYGEN_DIR}/ie_plugin_api.xml")
-
-    set(IE_LAYOUT_BINARY "${DOCS_BINARY_DIR}/ie_docs.xml")
-    set(C_LAYOUT_BINARY "${DOCS_BINARY_DIR}/ie_c_api.xml")
-    set(PY_LAYOUT_BINARY "${DOCS_BINARY_DIR}/ie_py_api.xml")
-    set(PLUGIN_LAYOUT_BINARY "${DOCS_BINARY_DIR}/ie_plugin_api.xml")
-
-    # Tables of contents
-    configure_file(${IE_LAYOUT_SOURCE} ${IE_LAYOUT_BINARY} @ONLY)
-    configure_file(${C_LAYOUT_SOURCE} ${C_LAYOUT_BINARY} @ONLY)
-    configure_file(${PY_LAYOUT_SOURCE} ${PY_LAYOUT_BINARY} @ONLY)
-    configure_file(${PLUGIN_LAYOUT_SOURCE} ${PLUGIN_LAYOUT_BINARY} @ONLY)
-
-    # Doxygen config files
-    configure_file(${IE_CONFIG_SOURCE} ${IE_CONFIG_BINARY} @ONLY)
-    configure_file(${C_CONFIG_SOURCE} ${C_CONFIG_BINARY} @ONLY)
-    configure_file(${PY_CONFIG_SOURCE} ${PY_CONFIG_BINARY} @ONLY)
-    configure_file(${PLUGIN_CONFIG_SOURCE} ${PLUGIN_CONFIG_BINARY} @ONLY)
-
-    # Preprocessing scripts
-    set(DOXY_MD_FILTER "${DOXYGEN_DIR}/doxy_md_filter.py")
-    set(PYX_FILTER "${DOXYGEN_DIR}/pyx_filter.py")
-
-    # C API
-
-    add_custom_target(c_api
-                      COMMAND ${DOXYGEN_EXECUTABLE} ${C_CONFIG_BINARY}
-                      WORKING_DIRECTORY ${DOCS_BINARY_DIR}
-                      COMMENT "Generating C API Reference"
-                      VERBATIM)
-
-    # Python API
-
-    add_custom_target(py_api
-                      COMMAND ${DOXYGEN_EXECUTABLE} ${PY_CONFIG_BINARY}
-                      WORKING_DIRECTORY ${DOCS_BINARY_DIR}
-                      COMMENT "Generating Python API Reference"
-                      VERBATIM)
-
-    add_custom_command(TARGET py_api
-                       PRE_BUILD
-                       COMMAND ${Python3_EXECUTABLE} ${PYX_FILTER} ${PYTHON_API_OUT}
-                       COMMENT "Pre-process Python API")
-
-    # Plugin API
-
-    add_custom_target(plugin_api
-                      COMMAND ${DOXYGEN_EXECUTABLE} ${PLUGIN_CONFIG_BINARY}
-                      WORKING_DIRECTORY ${DOCS_BINARY_DIR}
-                      COMMENT "Generating Plugin API Reference"
-                      VERBATIM)
-
-    # Preprocess docs
-
-    add_custom_target(preprocess_docs
-                      COMMENT "Pre-process docs"
-                      VERBATIM)
-
-    foreach(source_file ${doc_source_files})
-        list(APPEND commands COMMAND ${CMAKE_COMMAND} -E copy
-            "${OpenVINO_MAIN_SOURCE_DIR}/${source_file}" "${DOCS_BINARY_DIR}/${source_file}")
-    endforeach()
-
-    add_custom_command(TARGET preprocess_docs
-                       PRE_BUILD
-                       ${commands}
-                       COMMAND ${Python3_EXECUTABLE} ${DOXY_MD_FILTER} ${DOCS_BINARY_DIR}
-                       COMMENT "Pre-process markdown and image links")
-
-    # IE dev guide and C++ API
-
-    add_custom_target(ie_docs
-                      DEPENDS preprocess_docs
-                      COMMAND ${DOXYGEN_EXECUTABLE} ${IE_CONFIG_BINARY}
-                      WORKING_DIRECTORY ${DOCS_BINARY_DIR}
-                      VERBATIM)
-
-    # Umbrella OpenVINO target
-
-    add_custom_target(openvino_docs
-                      DEPENDS c_api py_api ie_docs plugin_api
-                      COMMENT "Generating OpenVINO documentation"
-                      VERBATIM)
-
-    set_target_properties(openvino_docs ie_docs c_api py_api preprocess_docs plugin_api
-                          PROPERTIES FOLDER docs)
-
-    find_program(browser NAMES xdg-open)
-    if(browser)
-        add_custom_target(ie_docs_open
-                          COMMAND ${browser} "${OpenVINO_MAIN_SOURCE_DIR}/docs/html/index.html"
-                          DEPENDS ie_docs
-                          COMMENT "Open OpenVINO documentation"
-                          VERBATIM)
-        set_target_properties(ie_docs_open PROPERTIES FOLDER docs)
-    endif()
-endfunction()
-
-if(ENABLE_DOCS)
-    build_docs()
+# Detect InferenceEngine
+find_package(InferenceEngine QUIET)
+if(NOT InferenceEngine_FOUND)
+    set(InferenceEngine_DIR ${CMAKE_BINARY_DIR})
+endif()
+
+add_subdirectory(template_extension)
+
+set(all_docs_targets
+    ie_docs_examples
+    template_extension
+    templatePlugin TemplateBehaviorTests TemplateFunctionalTests)
+foreach(target_name IN LISTS all_docs_targets)
+    if (TARGET ${target_name})
+        set_target_properties(${target_name} PROPERTIES FOLDER docs)
+    endif()
+endforeach()
+
+# OpenVINO docs
+
+set(OPENVINO_DOCS_PATH "" CACHE PATH "Path to openvino-documentation local repository")
+set(args "")
+
+if(OPENVINO_DOCS_PATH)
+    set(args "${args} ovinodoc_path:${OPENVINO_DOCS_PATH}")
+endif()
+
+file(GLOB_RECURSE docs_files "${OpenVINO_MAIN_SOURCE_DIR}/docs")
+file(GLOB_RECURSE include_files "${OpenVINO_MAIN_SOURCE_DIR}/inference-engine/include")
+file(GLOB_RECURSE ovino_files "${OPENVINO_DOCS_PATH}")
+
+add_custom_target(ie_docs
+                  COMMAND ./build_docs.sh ${args}
+                  WORKING_DIRECTORY "${OpenVINO_MAIN_SOURCE_DIR}/docs/build_documentation"
+                  COMMENT "Generating OpenVINO documentation"
+                  SOURCES ${docs_files} ${include_files} ${ovino_files}
+                  VERBATIM)
+set_target_properties(ie_docs PROPERTIES FOLDER docs)
+
+find_program(browser NAMES xdg-open)
+if(browser)
+    add_custom_target(ie_docs_open
+                      COMMAND ${browser} "${OpenVINO_MAIN_SOURCE_DIR}/doc/html/index.html"
+                      DEPENDS ie_docs
+                      COMMENT "Open OpenVINO documentation"
+                      VERBATIM)
+    set_target_properties(ie_docs_open PROPERTIES FOLDER docs)
 endif()

docs/HOWTO/Custom_Layers_Guide.md

@@ -21,11 +21,11 @@ The original format will be a supported framework such as TensorFlow, Caffe, or
 
 ## Custom Layer Overview
 
-The [Model Optimizer](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md) searches the list of known layers for each layer contained in the input model topology before building the model's internal representation, optimizing the model, and producing the Intermediate Representation files.  
+The [Model Optimizer](https://docs.openvinotoolkit.org/2019_R1.1/_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html) searches the list of known layers for each layer contained in the input model topology before building the model's internal representation, optimizing the model, and producing the Intermediate Representation files.  
 
-The [Inference Engine](../IE_DG/Deep_Learning_Inference_Engine_DevGuide.md) loads the layers from the input model IR files into the specified device plugin, which will search a list of known layer implementations for the device.  If your topology contains layers that are not in the list of known layers for the device, the Inference Engine considers the layer to be unsupported and reports an error.  To see the layers that are supported by each device plugin for the Inference Engine, refer to the [Supported Devices](../IE_DG/supported_plugins/Supported_Devices.md) documentation.  
+The [Inference Engine](https://docs.openvinotoolkit.org/2019_R1.1/_docs_IE_DG_Deep_Learning_Inference_Engine_DevGuide.html) loads the layers from the input model IR files into the specified device plugin, which will search a list of known layer implementations for the device.  If your topology contains layers that are not in the list of known layers for the device, the Inference Engine considers the layer to be unsupported and reports an error.  To see the layers that are supported by each device plugin for the Inference Engine, refer to the [Supported Devices](https://docs.openvinotoolkit.org/2019_R1.1/_docs_IE_DG_supported_plugins_Supported_Devices.html) documentation.  
 <br>
-> **NOTE:** If a device doesn't support a particular layer, an alternative to creating a new custom layer is to target an additional device using the HETERO plugin. The [Heterogeneous Plugin](../IE_DG/supported_plugins/HETERO.md) may be used to run an inference model on multiple devices allowing the unsupported layers on one device to "fallback" to run on another device (e.g., CPU) that does support those layers.
+**Note:** If a device doesn't support a particular layer, an alternative to creating a new custom layer is to target an additional device using the HETERO plugin. The [Heterogeneous Plugin](https://docs.openvinotoolkit.org/2019_R1.1/_docs_IE_DG_supported_plugins_HETERO.html) may be used to run an inference model on multiple devices allowing the unsupported layers on one device to "fallback" to run on another device (e.g., CPU) that does support those layers.
 
 ## Custom Layer Implementation Workflow
 
@@ -40,7 +40,7 @@ The following figure shows the basic processing steps for the Model Optimizer hi
 
 The Model Optimizer first extracts information from the input model which includes the topology of the model layers along with parameters, input and output format, etc., for each layer.  The model is then optimized from the various known characteristics of the layers, interconnects, and data flow which partly comes from the layer operation providing details including the shape of the output for each layer.  Finally, the optimized model is output to the model IR files needed by the Inference Engine to run the model.  
 
-The Model Optimizer starts with a library of known extractors and operations for each [supported model framework](../MO_DG/prepare_model/Supported_Frameworks_Layers.md) which must be extended to use each unknown custom layer.  The custom layer extensions needed by the Model Optimizer are:
+The Model Optimizer starts with a library of known extractors and operations for each [supported model framework](https://docs.openvinotoolkit.org/2019_R1.1/_docs_MO_DG_prepare_model_Supported_Frameworks_Layers.html) which must be extended to use each unknown custom layer.  The custom layer extensions needed by the Model Optimizer are:
 
 - Custom Layer Extractor
    - Responsible for identifying the custom layer operation and extracting the parameters for each instance of the custom layer.  The layer parameters are stored per instance and used by the layer operation before finally appearing in the output IR.  Typically the input layer parameters are unchanged, which is the case covered by this tutorial. 
@@ -182,10 +182,10 @@ There are two options to convert your MXNet* model that contains custom layers:
 2.  If you have sub-graphs that should not be expressed with the analogous sub-graph in the Intermediate Representation, but another sub-graph should appear in the model, the Model Optimizer provides such an option. In MXNet the function is actively used for ssd models provides an opportunity to  for the necessary subgraph sequences and replace them. To read more, see [Sub-graph Replacement in the Model Optimizer](../MO_DG/prepare_model/customize_model_optimizer/Subgraph_Replacement_Model_Optimizer.md).
 
 ## Kaldi\* Models with Custom Layers <a name="Kaldi-models-with-custom-layers"></a>
-For information on converting your Kaldi* model containing custom layers see [Converting a Kaldi Model in the Model Optimizer Developer Guide](../MO_DG/prepare_model/convert_model/Convert_Model_From_Kaldi.md). 
+For information on converting your Kaldi* model containing custom layers see [Converting a Kaldi Model in the Model Optimizer Developer Guide](https://docs.openvinotoolkit.org/latest/_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_Kaldi.html). 
 
 ## ONNX\* Models with Custom Layers <a name="ONNX-models-with-custom-layers"></a>
-For information on converting your ONNX* model containing custom layers see [Converting an ONNX Model in the Model Optimizer Developer Guide](../MO_DG/prepare_model/convert_model/Convert_Model_From_ONNX.md). 
+For information on converting your ONNX* model containing custom layers see [Converting an ONNX Model in the Model Optimizer Developer Guide](https://docs.openvinotoolkit.org/latest/_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_ONNX.html). 
 
 ## Step-by-Step Custom Layers Tutorial
 For a step-by-step walk-through creating and executing a custom layer, see [Custom Layer Implementation Tutorial for Linux and Windows.](https://github.com/david-drew/OpenVINO-Custom-Layers/tree/master/2019.r2.0) 
@@ -194,10 +194,10 @@ For a step-by-step walk-through creating and executing a custom layer, see [Cust
 
 - Intel® Distribution of OpenVINO™ toolkit home page: [https://software.intel.com/en-us/openvino-toolkit](https://software.intel.com/en-us/openvino-toolkit)
 - OpenVINO™ toolkit online documentation: [https://docs.openvinotoolkit.org](https://docs.openvinotoolkit.org)
-- [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
-- [Inference Engine Extensibility Mechanism](../IE_DG/Extensibility_DG/Intro.md)
-- [Inference Engine Samples Overview](../IE_DG/Samples_Overview.md)
-- [Overview of OpenVINO™ Toolkit Pre-Trained Models](@ref omz_models_intel_index)
+- [Model Optimizer Developer Guide](https://docs.openvinotoolkit.org/latest/_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html)
+- [Kernel Extensivility in the Inference Engine Developer Guide](https://docs.openvinotoolkit.org/latest/_docs_IE_DG_Integrate_your_kernels_into_IE.html)
+- [Inference Engine Samples Overview](https://docs.openvinotoolkit.org/latest/_docs_IE_DG_Samples_Overview.html)
+- [Overview of OpenVINO™ Toolkit Pre-Trained Models](https://docs.openvinotoolkit.org/latest/_intel_models_index.html)
 - [Inference Engine Tutorials](https://github.com/intel-iot-devkit/inference-tutorials-generic)
 - For IoT Libraries and Code Samples see the [Intel® IoT Developer Kit](https://github.com/intel-iot-devkit).
 

docs/IE_DG/API_Changes.md

@@ -2,122 +2,6 @@
 
 The sections below contain detailed list of changes made to the Inference Engine API in recent releases.
 
-## 2021.1
-
-### Deprecated API
-
- **Utility functions to convert Unicode paths**
-
- * InferenceEngine::stringToFileName - use OS-specific native conversion functions
- * InferenceEngine::fileNameToString - use OS-specific native conversion functions
-
-### Removed API
-
- **Plugin API:**
-
- * InferenceEngine::InferencePlugin C++ plugin wrapper class
- * InferenceEngine::IInferencePlugin plugin interface
- * InferenceEngine::PluginDispatcher class
- * InferenceEngine::InferenceEnginePluginPtr typedef
- * InferenceEngine::ICNNNetReader reader interface
- * InferenceEngine::CNNNetReader class
-
- **Extensibility API:**
-
- * InferenceEngine::ILayerImplFactory class
- * InferenceEngine::IShapeInferImpl class
- * InferenceEngine::IShapeInferExtension class
- * InferenceEngine::IExtension::getFactoryFor(ILayerImplFactory\*& factory, const CNNLayer\* cnnLayer, ResponseDesc\* resp) noexcept method
- * InferenceEngine::IExtension::getPrimitiveTypes(char\*\*& types, unsigned int& size, ResponseDesc\* resp) noexcept method
- * InferenceEngine::ShapeInferImpl class
- * InferenceEngine::Extension::getFactoryFor(ILayerImplFactory\*& factory, const CNNLayer\* cnnLayer, ResponseDesc\* resp) noexcept method
- * InferenceEngine::Extension::getPrimitiveTypes(char\*\*& types, unsigned int& size, ResponseDesc\* resp) noexcept method
-
- **Network API:**
-
- * InferenceEngine::details::CNNNetworkIterator class
- * InferenceEngine::CNNNetwork::getPrecision() const method
- * InferenceEngine::CNNNetwork::getLayerByName(const char\* layerName) const method
- * InferenceEngine::CNNNetwork::size() const method
- * InferenceEngine::CNNNetwork::begin() const method
- * InferenceEngine::CNNNetwork::end() const method
- * InferenceEngine::CNNNetwork::AddExtension(const IShapeInferExtensionPtr& extension) method
- * InferenceEngine::ICNNNetwork::getPrecision() const noexcept method
- * InferenceEngine::ICNNNetwork::getName(char\* pName, size_t len) const noexcept method
- * InferenceEngine::ICNNNetwork::getData(const char\* dname) noexcept method
- * InferenceEngine::ICNNNetwork::addLayer(const CNNLayerPtr& layer) noexcept method
- * InferenceEngine::ICNNNetwork::getLayerByName(const char\* layerName, CNNLayerPtr& out, ResponseDesc\* resp) const noexcept method
- * InferenceEngine::ICNNNetwork::AddExtension(const IShapeInferExtensionPtr& extension, ResponseDesc\* resp) noexcept method
- * InferenceEngine::ICNNNetwork::getStats(ICNNNetworkStats\*\* stats, ResponseDesc\* resp) const noexcept method
- * InferenceEngine::ICNNNetworkStats class
- * InferenceEngine::NetworkNodeStats class
- * InferenceEngine::Data::getCreatorLayer() method
- * InferenceEngine::Data::getInputTo() method
- * InferenceEngine::LayerParams class
-
- **Layer API:**
-
- * InferenceEngine::CNNLayer class
- * InferenceEngine::WeightableLayer class
- * InferenceEngine::BatchNormalizationLayer class
- * InferenceEngine::BatchToSpaceLayer class
- * InferenceEngine::BinaryConvolutionLayer class
- * InferenceEngine::BroadcastLayer class
- * InferenceEngine::BucketizeLayer class
- * InferenceEngine::ClampLayer class
- * InferenceEngine::ConcatLayer class
- * InferenceEngine::ConvolutionLayer class
- * InferenceEngine::CropLayer class
- * InferenceEngine::DeconvolutionLayer class
- * InferenceEngine::DeformableConvolutionLayer class
- * InferenceEngine::DepthToSpaceLayer class
- * InferenceEngine::EltwiseLayer class
- * InferenceEngine::ExperimentalDetectronPriorGridGenerator class
- * InferenceEngine::ExperimentalDetectronPriorGridGeneratorLayer class
- * InferenceEngine::ExperimentalSparseWeightedReduceLayer class
- * InferenceEngine::FillLayer class
- * InferenceEngine::FullyConnectedLayer class
- * InferenceEngine::GRNLayer class
- * InferenceEngine::GRUCell class
- * InferenceEngine::GatherLayer class
- * InferenceEngine::GemmLayer class
- * InferenceEngine::LSTMCell class
- * InferenceEngine::MVNLayer class
- * InferenceEngine::MathLayer class
- * InferenceEngine::NonMaxSuppression class
- * InferenceEngine::NormLayer class
- * InferenceEngine::OneHotLayer class
- * InferenceEngine::PReLULayer class
- * InferenceEngine::PadLayer class
- * InferenceEngine::PoolingLayer class
- * InferenceEngine::PowerLayer class
- * InferenceEngine::QuantizeLayer class
- * InferenceEngine::RNNCell class
- * InferenceEngine::RNNCellBase class
- * InferenceEngine::RNNSequenceLayer class
- * InferenceEngine::RangeLayer class
- * InferenceEngine::ReLU6Layer class
- * InferenceEngine::ReLULayer class
- * InferenceEngine::ReduceLayer class
- * InferenceEngine::ReshapeLayer class
- * InferenceEngine::ReverseSequenceLayer class
- * InferenceEngine::ScaleShiftLayer class
- * InferenceEngine::ScatterLayer class
- * InferenceEngine::SelectLayer class
- * InferenceEngine::ShuffleChannelsLayer class
- * InferenceEngine::SoftMaxLayer class
- * InferenceEngine::SpaceToBatchLayer class
- * InferenceEngine::SpaceToDepthLayer class
- * InferenceEngine::SparseFillEmptyRowsLayer class
- * InferenceEngine::SparseSegmentReduceLayer class
- * InferenceEngine::SparseToDenseLayer class
- * InferenceEngine::SplitLayer class
- * InferenceEngine::StridedSliceLayer class
- * InferenceEngine::TensorIterator class
- * InferenceEngine::TileLayer class
- * InferenceEngine::TopKLayer class
- * InferenceEngine::UniqueLayer class
-
 ## 2020.4
 
 ### New API

docs/IE_DG/Deep_Learning_Inference_Engine_DevGuide.md

@@ -22,7 +22,7 @@ The OpenVINO™ toolkit includes the following components:
     TensorFlow*, MXNet*, Kaldi*, ONNX* models.
     - [Deep Learning Inference Engine](inference_engine_intro.md) — A unified API to allow high performance inference on many hardware types
     including Intel® CPU, Intel® Processor Graphics, Intel® FPGA, Intel® Neural Compute Stick 2.
-    - [nGraph](../nGraph_DG/nGraph_dg.md) — graph representation and manipulation engine which is used to represent a model inside Inference Engine and allows the run-time model construction without using Model Optimizer.
+    - [nGraph](nGraph_Flow.md) — graph representation and manipulation engine which is used to represent a model inside Inference Engine and allows the run-time model construction without using Model Optimizer.
 * [OpenCV](https://docs.opencv.org/) — OpenCV* community version compiled for Intel® hardware.
 Includes PVL libraries for computer vision.
 * Drivers and runtimes for OpenCL™ version 2.1
@@ -42,10 +42,14 @@ inference of a pre-trained and optimized deep learning model and a set of sample
 
 ## Table of Contents
 
+* [Introduction to Intel® Deep Learning Deployment Toolkit](Introduction.md)
+
 * [Inference Engine API Changes History](API_Changes.md)
 
 * [Introduction to Inference Engine](inference_engine_intro.md)
 
+* [Introduction to nGraph Flow](nGraph_Flow.md)
+
 * [Understanding Inference Engine Memory Primitives](Memory_primitives.md)
 
 * [Introduction to Inference Engine Device Query API](InferenceEngine_QueryAPI.md)
@@ -54,7 +58,7 @@ inference of a pre-trained and optimized deep learning model and a set of sample
 
 * [Integrating Inference Engine in Your Application](Integrate_with_customer_application_new_API.md)
 
-* [[DEPRECATED] Migration from Inference Engine Plugin API to Core API](Migration_CoreAPI.md)
+* [Migration from Inference Engine Plugin API to Core API](Migration_CoreAPI.md)
 
 * [Introduction to Performance Topics](Intro_to_Performance.md)
 
@@ -74,15 +78,16 @@ inference of a pre-trained and optimized deep learning model and a set of sample
 * [Supported Devices](supported_plugins/Supported_Devices.md)
     * [GPU](supported_plugins/CL_DNN.md)
     * [CPU](supported_plugins/CPU.md)
+    * [FPGA](supported_plugins/FPGA.md)
     * [VPU](supported_plugins/VPU.md)
       * [MYRIAD](supported_plugins/MYRIAD.md)
       * [HDDL](supported_plugins/HDDL.md)
     * [Heterogeneous execution](supported_plugins/HETERO.md)
     * [GNA](supported_plugins/GNA.md)
-    * [MULTI](supported_plugins/MULTI.md)
+    * **NEW!** [MULTI](supported_plugins/MULTI.md)
 
 * [Pre-Trained Models](@ref omz_models_intel_index)
 
 * [Known Issues](Known_Issues_Limitations.md)
 
-**Typical Next Step:** [Introduction to Inference Engine](inference_engine_intro.md)
+**Typical Next Step:** [Introduction to Intel® Deep Learning Deployment Toolkit](Introduction.md)

docs/IE_DG/Extensibility_DG/AddingNGraphOps.md

@@ -12,12 +12,10 @@ To add your custom nGraph operation, create a new class that extends `ngraph::Op
 
 3. Override the shape inference method `validate_and_infer_types`. This method is called multiple times during graph manipulations to determine the shapes and element types of the outputs of the operations. You can access the input shapes through the `get_input_partial_shape()` method and input element types through the `get_input_element_type()` method of `ngraph::Node`. Set the inferred shape and element type of the output using `set_output_type`.
 
-4. Override the `clone_with_new_inputs` method, which allows graph manipulation routines to create copies of this operation and connect it to different nodes during optimization.
+4. Override the `copy_with_new_args` method, which allows graph manipulation routines to create copies of this operation and connect it to different nodes during optimization.
 
 5. Override the `visit_attributes` method, which allows serialization and deserialization of attributes. An `AttributeVisitor` is passed to the method, and the implementation is expected to walk over all the attributes in the op using the type-aware `on_attribute` helper. Helpers are already implemented for standard C++ types like `int64_t`, `float`, `bool`, `vector` and for existing nGraph defined types.
 
-6. Override `evaluate`, which is an optional method that enables the application of constant folding if there is a custom operation on the constant branch.
-
 Based on that, declaration of a operation class can look as follows:
 
 @snippet op.hpp op:header
@@ -41,9 +39,9 @@ nGraph operation contains two constructors: a default constructor, which allows
 
 @snippet op.cpp op:validate
 
-### `clone_with_new_inputs()`
+### `copy_with_new_args()`
 
-`ngraph::Node::clone_with_new_inputs` method creates a copy of nGraph operation with new inputs.
+`ngraph::Node::copy_with_new_args` method creates a copy of nGraph operation with new inputs.
 
 @snippet op.cpp op:copy
 
@@ -53,12 +51,6 @@ nGraph operation contains two constructors: a default constructor, which allows
 
 @snippet op.cpp op:visit_attributes
 
-### `evaluate()`
-
-`ngraph::Node::evaluate` method allows to apply constant folding to an operation.
-
-@snippet op.cpp op:evaluate
-
 ## Register Custom Operations in Extension Class
 
 To add custom operations to the [Extension](Extension.md) class, create an operation set with custom operations and implement the `InferenceEngine::IExtension::getOpSets` method:
@@ -77,4 +69,4 @@ When specifying opset names, follow the rules below:
 * `opset1` is the name of default operations set.
 Operations from the default opset cannot be redefined.
 
-Use a custom opset to create a new operation or extend functionality of an existing operation from another opset.
+Use a custom opset to create a new operation or extend functionality of an existing operation from another opset.

docs/IE_DG/Extensibility_DG/Intro.md

@@ -40,6 +40,13 @@ The following pages describe how to integrate custom _kernels_ into the Inferenc
  * [Introduction to development of custom GPU kernels](GPU_Kernel.md)
  * [Introduction to development of custom VPU kernels](VPU_Kernel.md)
 
+## Deprecated Extensibility API
+
+Shape Inference API and some methods of extensibility mechanism was deprecated and will be removed soon.
+Old Extensibility mechanism contains two parts shape inference and execution kernel.
+ * [Shape Inference](deprecated/ShapeInfer.md)
+ * [Execution Kernel](deprecated/Factory.md)
+
 ## Additional Resources
 
 * [Build an extension library using CMake*](Building.md)

docs/IE_DG/Extensibility_DG/deprecated/Factory.md

@@ -0,0 +1,96 @@
+# Deprecated API for CPU kernels creation {#openvino_docs_IE_DG_Extensibility_DG_deprecated_Factory}
+
+List of deprecated API for kernels development:
+ * `InferenceEngine::IExtension::getPrimitiveTypes(char**& types, unsigned int& size, ResponseDesc* resp)` method
+ * `InferenceEngine::IExtension::getFactoryFor(ILayerImplFactory *&factory, const CNNLayer *cnnLayer, ResponseDesc *resp)` method
+ * `InferenceEngine::ILayerImplFactory` class
+
+>**NOTE**: This guide demonstrates how to use deprecated API for kernels creation. However, keep in mind that this API will be deleted soon.
+
+1. Create your custom layer factory `CustomLayerFactory` class:
+```cpp
+// custom_layer.h
+// A CustomLayerFactory class is an example layer, which makes exponentiation by 2 for the input and does not change dimensions
+class CustomLayerFactory {
+
+};
+```
+2. Inherit it from the abstract `InferenceEngine::ILayerImplFactory` class: 
+```cpp
+// custom_layer.h
+class CustomLayerFactory: public InferenceEngine::ILayerImplFactory {
+
+};
+```
+
+3. Create a constructor, a virtual destructor, and a data member to keep the layer info:
+```cpp
+// custom_layer.h
+class CustomLayerFactory: public InferenceEngine::ILayerImplFactory {
+public:
+    explicit CustomLayerFactory(const CNNLayer *layer): cnnLayer(*layer) {}
+private:
+  CNNLayer cnnLayer;
+};
+```
+
+4. Overload and implement the abstract methods `getShapes` and `getImplementations` of the `InferenceEngine::ILayerImplFactory` class:
+```cpp
+// custom_layer.h
+class CustomLayerFactory: public InferenceEngine::ILayerImplFactory {
+public:
+    // ... constructor and destructor
+
+    StatusCode getShapes(const std::vector<TensorDesc>& inShapes, std::vector<TensorDesc>& outShapes, ResponseDesc *resp) noexcept override {
+        if (cnnLayer == nullptr) {
+            std::string errorMsg = "Cannot get cnn layer!";
+            errorMsg.copy(resp->msg, sizeof(resp->msg) - 1);
+            return GENERAL_ERROR;
+        }
+        if (inShapes.size() != 1) {
+            std::string errorMsg = "Incorrect input shapes!";
+            errorMsg.copy(resp->msg, sizeof(resp->msg) - 1);
+            return GENERAL_ERROR;
+        }
+        outShapes.clear();
+        outShapes.emplace_back(inShapes[0]);
+        return OK;
+    }
+
+    StatusCode getImplementations(std::vector<ILayerImpl::Ptr>& impls, ResponseDesc *resp) noexcept override {
+        // You can add cnnLayer to implementation if it is necessary
+        impls.push_back(ILayerImpl::Ptr(new CustomLayerImpl()));
+        return OK;
+    }
+};
+```
+5. Create your custom layer implementation `CustomLayerImpl` class using the [instruction](../CPU_Kernel.md).
+
+6. Implement methods in the `Extension` class:
+```cpp
+// custom_extension.h
+class CustomExtention : public InferenceEngine::IExtension {
+public:
+    // ... utility methods
+    // Retruns the list of supported kernels/layers
+    StatusCode getPrimitiveTypes(char**& types, unsigned int& size, ResponseDesc* resp) noexcept override {
+        std::string type_name = "CustomLayer";
+        types = new char *[1];
+        size = 1;
+        types[0] = new char[type_name.size() + 1];
+        std::copy(type_name.begin(), type_name.end(), types[0]);
+        types[0][type_name.size()] = '\0';
+        return OK;
+    }
+    // Main function
+    StatusCode getFactoryFor(ILayerImplFactory *&factory, const CNNLayer *cnnLayer, ResponseDesc *resp) noexcept override {
+        if (cnnLayer->type != "CustomLayer") {
+            std::string errorMsg = std::string("Factory for ") + cnnLayer->type + " wasn't found!";
+            errorMsg.copy(resp->msg, sizeof(resp->msg) - 1);
+            return NOT_FOUND;
+        }
+        factory = new CustomLayerFactory(cnnLayer);
+        return OK;
+    }
+};
+```

docs/IE_DG/Extensibility_DG/deprecated/ShapeInfer.md

@@ -0,0 +1,18 @@
+# Old ShapeInference Extensibility API {#openvino_docs_IE_DG_Extensibility_DG_deprecated_ShapeInfer}
+
+The new approach to shape inference suggests a creation of a custom nGraph operation that contains a special method for shape inference. 
+The following classes and methods were deprecated:
+
+ * `InferenceEngine::IShapeInferExtension` class
+ * `InferenceEngine::IShapeInferExtension::getShapeInferTypes(char**&, unsigned int&, ResponseDesc*)` method
+ * `InferenceEngine::IShapeInferExtension::getShapeInferImpl(IShapeInferImpl::Ptr&, const char*, ResponseDesc*)` method
+
+However, the old approach with the `InferenceEngine::IShapeInferExtension` method still works for already existing custom layers.
+Custom Shape Inference functions are registered by calling `InferenceEngine::ICNNNetwork::AddExtension` with the implemented `InferenceEngine::IShapeInferExtension` method, which is a holder of custom implementations. 
+The holder requires to implement two key methods:
+* `InferenceEngine::IShapeInferExtension::getShapeInferImpl` - Returns custom shape inference implementation for the given type. 
+* `InferenceEngine::IShapeInferExtension::getShapeInferTypes` - Provides all custom types.
+
+Custom shape inference implementation is represented by the `InferenceEngine::IShapeInferImpl::inferShapes` method.
+
+It is impossible to overwrite built-in shape inference functions. Custom type must be different from the supported ones. 

docs/IE_DG/Glossary.md

@@ -64,15 +64,17 @@ Glossary of terms used in the Inference Engine
 | :---                        | :---                |
 | Batch | Number of images to analyze during one call of infer. Maximum batch size is a property of the network and it is set before loading of the network to the plugin. In NHWC, NCHW and NCDHW image data layout representation, the N refers to the number of images in the batch |
 | Blob | Memory container used for storing inputs, outputs of the network, weights and biases of the layers |
-| Device (Affinitity) | A preferred Intel(R) hardware device to run the inference (CPU, GPU, etc.) |
+| Device (Affinitity) | A preferred Intel(R) hardware device to run the inference (CPU, GPU, FPGA, etc.) |
 | Extensibility mechanism, Custom layers | The mechanism that provides you with capabilities to extend the Inference Engine and Model Optimizer so that they can work with topologies containing layers that are not yet supported |
 | <code>ICNNNetwork</code> | An Interface of the Convolutional Neural Network that Inference Engine reads from IR. Consists of topology, weights and biases |
 | <code>IExecutableNetwork</code> | An instance of the loaded network which allows the Inference Engine to request (several) infer requests and perform inference synchronously or asynchronously |
+| <code>IHeteroInferencePlugin</code> |  Interface that is implemented by the heterogeneity plugin to allow the Inference Engine to set the default affinities for layers by devices before loading the network to the heterogeneous plugin. You can modify affinities manually before loading to the plugin. |
+| <code>IInferencePlugin</code> | Interface provided by each plugin to allow the Inference Engine to load <code>ICNNNetwork</code> to the plugin, create Executable network and set special dedicated options for the plugin |
 | <code>IInferRequest</code> | Interface that represents the end point of inference on the model loaded to the plugin and represented by executable network. Inputs are set here, outputs should be requested from this interface as well |
 | <code>InferenceEngineProfileInfo</code> | Represents basic inference profiling information per layer |
 | Inference Engine | A C++ library with a set of classes that you can use in your application to infer input data (images) and get the result |
 | Inference Engine API | The basic default API for all supported devices, which allows you to load a model from Intermediate Representation, set input and output formats and execute the model on various devices |
-| Inference Engine <code>Core<code> | Inference Engine Core is a software component that manages inference on certain Intel(R) hardware devices: CPU, GPU, MYRIAD, GNA, etc. |
+| Inference Engine Plugin | Inference Engine plugin is a software component that contains complete implementation for inference on a certain Intel(R) hardware device: CPU, GPU, VPU, FPGA, etc. Each plugin implements the unified API and provides additional hardware-specific APIs. |
 | Layer catalog or Operations specification | A list of supported layers or operations and its parameters. Sets of supported layers are different for different plugins, please check the documentation on plugins to verify if the Inference Engine supports certain layer on the dedicated hardware |
 | <code>Layout</code> | Image data layout refers to the representation of images batch. Layout shows a sequence of 4D or 5D tensor data in memory. A typical NCHW format represents pixel in horizontal direction, rows by vertical dimension, planes by channel and images into batch |
 | <code>OutputsDataMap</code> | Structure which contains information about output precisions and layouts |

docs/IE_DG/Graph_debug_capabilities.md

@@ -0,0 +1,47 @@
+# Graph Debug Capabilities {#openvino_docs_IE_DG_Graph_debug_capabilities}
+
+Inference Engine supports two different objects for a graph representation: the nGraph function and 
+CNNNetwork. Both representations provide an API to get detailed information about the graph structure.
+
+## nGraph Function
+
+To receive additional messages about applied graph modifications, rebuild the nGraph library with 
+the `-DNGRAPH_DEBUG_ENABLE=ON` option.
+
+To enable serialization and deserialization of the nGraph function to a JSON file, rebuild the 
+nGraph library with the `-DNGRAPH_JSON_ENABLE=ON` option. To serialize or deserialize the nGraph
+function, call the nGraph function as follows:
+
+```cpp
+#include <ngraph/serializer.hpp>
+
+std::shared_ptr<ngraph::Function> nGraph;
+...
+ngraph::serialize("test_json.json", nGraph);        // For graph serialization
+std::ifstream file("test_json.json");               // Open a JSON file
+nGraph = ngraph::deserialize(file);                 // For graph deserialization
+```
+
+To visualize the nGraph function to the xDot format or to an image file, use the 
+`ngraph::pass::VisualizeTree` graph transformation pass:
+```cpp
+#include <ngraph/pass/visualize_tree.hpp>
+
+std::shared_ptr<ngraph::Function> nGraph;
+...
+std::vector<std::shared_ptr<ngraph::Function>> g2{nGraph};
+ngraph::pass::VisualizeTree("after.png").run_on_module(g2);     // Visualize the nGraph function to an image
+```
+
+## CNNNetwork
+
+To serialize the CNNNetwork to the Inference Engine Intermediate Representation (IR) format, use the 
+`CNNNetwork::serialize(...)` method:
+```cpp
+std::shared_ptr<ngraph::Function> nGraph;
+...
+CNNNetwork network(nGraph);
+network.serialize("test_ir.xml", "test_ir.bin");
+```
+> **NOTE**: CNNNetwork created from the nGraph function might differ from the original nGraph 
+> function because the Inference Engine applies some graph transformation.

docs/IE_DG/Integrate_with_customer_application_new_API.md

@@ -5,6 +5,9 @@ This section provides a high-level description of the process of integrating the
 Refer to the [Hello Classification Sample](../../inference-engine/samples/hello_classification/README.md) sources
 for example of using the Inference Engine in applications.
 
+> **NOTE**: For 2019 R2 Release, the new Inference Engine Core API is introduced. This guide is updated to reflect the new API approach.
+> The Inference Engine Plugin API is still supported, but is going to be deprecated in future releases. Please, refer to [Migration from Inference Engine Plugin API to Core API](Migration_CoreAPI.md) guide to update your application.
+
 ## Use the Inference Engine API in Your Code
 
 The core `libinference_engine.so` library implements loading and parsing a model Intermediate Representation (IR), and triggers inference using a specified device. The core library has the following API:
@@ -36,7 +39,7 @@ InferenceEngine::Core core;
 ```cpp
 auto network = core.ReadNetwork("Model.xml");
 ```
-**Or read the model from ONNX format** (.onnx and .prototxt are supported formats). You can find more information about the ONNX format support in the document [ONNX format support in the OpenVINO™](./ONNX_Support.md).
+**Or read the model from ONNX format** (.onnx and .prototxt are supported formats)
 ```cpp
 auto network = core.ReadNetwork("model.onnx");
 ```
@@ -269,7 +272,7 @@ build/                  - build directory
 ```
 
 2. **Include Inference Engine, nGraph and OpenCV libraries** in `project/CMakeLists.txt`  
-[OpenCV](https://docs.opencv.org/master/db/df5/tutorial_linux_gcc_cmake.html) integration is needed mostly for pre-processing input data and ngraph for more complex applications using [ngraph API](../nGraph_DG/nGraph_dg.md).
+[OpenCV](https://docs.opencv.org/master/db/df5/tutorial_linux_gcc_cmake.html) integration is needed mostly for pre-processing input data and ngraph for more complex applications using [ngraph API](nGraph_Flow.md).
 ``` cmake
 cmake_minimum_required(VERSION 3.0.0)
 project(project_name)

docs/IE_DG/Intro_to_Performance.md

@@ -27,7 +27,7 @@ latency penalty. So, for more real-time oriented usages, lower batch sizes (as l
 Refer to the [Benchmark App](../../inference-engine/samples/benchmark_app/README.md) sample, which allows latency vs. throughput measuring.
 
 ## Using Async API
-To gain better performance on accelerators, such as VPU, the Inference Engine uses the asynchronous approach (see
+To gain better performance on accelerators, such as VPU or FPGA, the Inference Engine uses the asynchronous approach (see
 [Integrating Inference Engine in Your Application (current API)](Integrate_with_customer_application_new_API.md)).
 The point is amortizing the costs of data transfers, by pipe-lining, see [Async API explained](@ref omz_demos_object_detection_demo_ssd_async_README).
 Since the pipe-lining relies on the availability of the parallel slack, running multiple inference requests in parallel is essential.

docs/IE_DG/Introduction.md

@@ -94,13 +94,13 @@ Refer to a dedicated description about [Intermediate Representation and Operatio
 OpenVINO toolkit is powered by nGraph capabilities for Graph construction API, Graph transformation engine and Reshape.
 nGraph Function is used as an intermediate representation for a model in the run-time underneath the CNNNetwork API.
 The conventional representation for CNNNetwork is still available if requested for backward compatibility when some conventional API methods are used.
-Please refer to the [Overview of nGraph](../nGraph_DG/nGraph_dg.md) describing the details of nGraph representation.
+Please refer to the [Overview of nGraph Flow](nGraph_Flow.md) describing the details of nGraph integration into the Inference Engine and co-existence with the conventional representation.
 
 ## Inference Engine <a name = "IE"></a>
 
 Inference Engine is a runtime that delivers a unified API to integrate the inference with application logic:
 
-* Takes a model as an input. The model can be presented in [the native ONNX format](./ONNX_Support.md) or in the specific form of [Intermediate Representation (IR)](../MO_DG/IR_and_opsets.md)
+* Takes as input the model. The model presented in the specific form of [Intermediate Representation (IR)](../MO_DG/IR_and_opsets.md)
 produced by Model Optimizer.
 * Optimizes inference execution for target hardware.
 * Delivers inference solution with reduced footprint on embedded inference platforms.
@@ -116,7 +116,7 @@ For Intel® Distribution of OpenVINO™ toolkit, the Inference Engine package co
 [sample console applications](Samples_Overview.md) demonstrating how you can use
 the Inference Engine in your applications.
 
-The open source version is available in the [OpenVINO™ toolkit GitHub repository](https://github.com/openvinotoolkit/openvino) and can be built for supported platforms using the <a href="https://github.com/openvinotoolkit/openvino/wiki/BuildingCode">Inference Engine Build Instructions</a>.
+The open source version is available in the [OpenVINO™ toolkit GitHub repository](https://github.com/openvinotoolkit/openvino) and can be built for supported platforms using the <a href="https://github.com/openvinotoolkit/openvino/blob/master/build-instruction.md">Inference Engine Build Instructions</a>.
 ## See Also
 - [Inference Engine Samples](Samples_Overview.md)
 - [Intel&reg; Deep Learning Deployment Toolkit Web Page](https://software.intel.com/en-us/computer-vision-sdk)

docs/IE_DG/Migration_CoreAPI.md

@@ -1,4 +1,4 @@
-[DEPRECATED] Migration from Inference Engine Plugin API to Core API {#openvino_docs_IE_DG_Migration_CoreAPI}
+Migration from Inference Engine Plugin API to Core API {#openvino_docs_IE_DG_Migration_CoreAPI}
 ===============================
 
 For 2019 R2 Release, the new Inference Engine Core API is introduced. This guide is updated to reflect the new API approach. The Inference Engine Plugin API is still supported, but is going to be deprecated in future releases.
@@ -45,7 +45,7 @@ read networks using the Core class:
 ```cpp
 CNNNetwork network = core.ReadNetwork(input_model);
 ```
-The Core class also allows reading models from the ONNX format (more information is [here](./ONNX_Support.md)):
+The Core class also allows reading models from ONNX format:
 ```cpp
 CNNNetwork network = core.ReadNetwork("model.onnx");
 ```

docs/IE_DG/ONNX_Support.md

@@ -1,19 +0,0 @@
-# ONNX format support in the OpenVINO™ {#openvino_docs_IE_DG_ONNX_Support}
-
-Starting from the 2020.4 release, OpenVINO™ supports reading native ONNX models.
-`Core::ReadNetwork()` method provides a uniform way to read models from IR or ONNX format, it is a recommended approach to reading models.
-Example:
-
-```cpp
-InferenceEngine::Core core;
-auto network = core.ReadNetwork("model.onnx");
-```
-
-OpenVINO™ doesn't provide a mechanism to specify pre-processing (like mean values subtraction, reverse input channels) for the ONNX format.
-If an ONNX model contains dynamic shapes for input, please use the `CNNNetwork::reshape` method for shape specialization.
-
-Unsupported types of tensors:
-
-* `string`,
-* `complex64`,
-* `complex128`.

docs/IE_DG/OnnxImporterTutorial.md

@@ -2,7 +2,7 @@
 
 > **NOTE**: This tutorial is deprecated. Since OpenVINO™ 2020.4 version, Inference Engine enables reading ONNX models via the Inference Engine Core API
 > and there is no need to use directly the low-level ONNX* Importer API anymore. 
-> To read ONNX\* models, it's recommended to use the `Core::ReadNetwork()` method that provide a uniform way to read models from IR or ONNX format.
+> To read ONNX\* models, it's recommended to use the InferenceEngine::Core::ReadNetwork method that provide a uniform way to read models from IR or ONNX format.
 
 This tutorial demonstrates how to use the ONNX\* Importer API.
 This API makes it possible to create an nGraph `Function` object from an imported ONNX model.
@@ -61,7 +61,7 @@ Refer to the sections below for details.
 > ```
 
 Once you create the `ng_function`, you can use it to run computation on the Inference Engine.
-As it was shown in [Build a Model with nGraph Library](../nGraph_DG/build_function.md), `std::shared_ptr<ngraph::Function>` can be transformed into a `CNNNetwork`.
+As it was shown in [Build a Model with nGraph Library](nGraphTutorial.md), `std::shared_ptr<ngraph::Function>` can be transformed into a `CNNNetwork`.
 
 
 ### <a name="stream">Stream as Input</a>
@@ -97,4 +97,4 @@ const std::shared_ptr<ngraph::Function> ng_function = ngraph::onnx_import::impor
 ```
 
 [onnx_header]: https://github.com/NervanaSystems/ngraph/blob/master/src/ngraph/frontend/onnx_import/onnx.hpp
-[onnx_model_zoo]: https://github.com/onnx/models
+[onnx_model_zoo]: https://github.com/onnx/models

docs/IE_DG/PythonPackage_Overview.md

@@ -12,4 +12,4 @@ The OpenVINO™ Python\* package includes the following sub-packages:
  - `openvino.tools.benchmark` - Measure latency and throughput.
 
 ## See Also
-* [Introduction to Inference Engine](inference_engine_intro.md)
+* [Introduction to Intel's Deep Learning Inference Engine](Introduction.md)

docs/IE_DG/Samples_Overview.md

@@ -49,11 +49,9 @@ You can download the [pre-trained models](@ref omz_models_intel_index) using the
 
 The officially supported Linux* build environment is the following:
 
-* Ubuntu* 18.04 LTS 64-bit or CentOS* 7.6 64-bit
-* GCC* 7.5.0 (for Ubuntu* 18.04) or GCC* 4.8.5 (for CentOS* 7.6)
-* CMake* version 3.10 or higher
-
-> **NOTE**: For building samples from the open-source version of OpenVINO™ toolkit, see the [build instructions on GitHub](https://github.com/openvinotoolkit/openvino/wiki/BuildingCode).
+* Ubuntu* 16.04 LTS 64-bit or CentOS* 7.4 64-bit
+* GCC* 5.4.0 (for Ubuntu* 16.04) or GCC* 4.8.5 (for CentOS* 7.4)
+* CMake* version 2.8.12 or higher
 
 To build the C or C++ sample applications for Linux, go to the `<INSTALL_DIR>/inference_engine/samples/c` or `<INSTALL_DIR>/inference_engine/samples/cpp` directory, respectively, and run the `build_samples.sh` script:
 ```sh
@@ -101,7 +99,7 @@ for the debug configuration — in `<path_to_build_directory>/intel64/Debug/`.
 The recommended Windows* build environment is the following:
 * Microsoft Windows* 10
 * Microsoft Visual Studio* 2017, or 2019
-* CMake* version 3.10 or higher
+* CMake* version 2.8.12 or higher
 
 > **NOTE**: If you want to use Microsoft Visual Studio 2019, you are required to install CMake 3.14.
 
@@ -183,4 +181,4 @@ sample, read the sample documentation by clicking the sample name in the samples
 list above.
 
 ## See Also
-* [Introduction to Inference Engine](inference_engine_intro.md)
+* [Introduction to Intel's Deep Learning Inference Engine](Introduction.md)

docs/IE_DG/ShapeInference.md

@@ -1,61 +1,38 @@
 Using Shape Inference {#openvino_docs_IE_DG_ShapeInference}
 ==========================================
 
-Inference Engine takes three kinds of a model description as an input, which are converted into an `InferenceEngine::CNNNetwork` object:
-1. [Intermediate Representation (IR)](../MO_DG/IR_and_opsets.md) through `InferenceEngine::Core::ReadNetwork`
-2. [ONNX model](../IE_DG/OnnxImporterTutorial.md) through `InferenceEngine::Core::ReadNetwork`
-3. [nGraph::Function](../nGraph_DG/nGraph_dg.md) through the constructor of `InferenceEngine::CNNNetwork`
+Inference Engine takes two kinds of model description as an input: [Intermediate Representation (IR)](../MO_DG/IR_and_opsets.md) and [nGraph::Function](nGraph_Flow.md) objects. 
+Both should have fixed input shapes to be successfully loaded to the Inference Engine.
+To feed input data of a shape that is different from the model input shape, resize the model first.
 
-`InferenceEngine::CNNNetwork` keeps an `ngraph::Function` object with the model description internally.
-The object should have fully defined input shapes to be successfully loaded to the Inference Engine plugins.
-To resolve undefined input dimensions of a model, call the `CNNNetwork::reshape` method providing new input shapes before loading to the Inference Engine plugin.
+Model resizing on the stage of <a href="_docs_MO_DG_prepare_model_convert_model_Converting_Model_General.html#when_to_specify_input_shapes">IR generation</a> or [nGraph::Function creation](nGraphTutorial.md) is the recommended approach. 
+OpenVINO™ provides the following experimental methods for runtime model reshaping:
 
-Run the following code right after `InferenceEngine::CNNNetwork` creation to explicitly check for model input names and shapes:
-```cpp
-CNNNetwork network = ... // read IR / ONNX model or create from nGraph::Function explicitly
-const auto parameters = network.getFunction()->get_parameters();
-for (const auto & parameter : parameters) {
-    std::cout << "name: " << parameter->get_friendly_name() << " shape: " << parameter->get_partial_shape() << std::endl;
-    if (parameter->get_partial_shape().is_dynamic())
-        std::cout << "ATTENTION: Input shape is not fully defined. Use the CNNNetwork::reshape method to resolve it." << std::endl;
-}
-```
+1.  Setting a new input shape with the `InferenceEngine::CNNNetwork::reshape` method
+ 
+	`InferenceEngine::CNNNetwork::reshape` method updates input shapes and propagates them down to the outputs of the model through all intermediate layers.
+    
+    Shape propagation for `InferenceEngine::CNNNetwork` objects created from `nGraph::Function` or IR of the version 10 works through the `nGraph` shape inference mechanism. 
+    `InferenceEngine::CNNNetwork` objects created from lower IR versions are considered deprecated and may be reshaped incorrectly or give unexpected results.
+ 
+	To keep the v10 IR resizable by the `InferenceEngine::CNNNetwork::reshape` method, convert the model with the additional Model Optimizer key `--keep_shape_ops`.
+ 
+2.  Setting a new batch dimension value with the `InferenceEngine::CNNNetwork::setBatchSize` method
+    
+    The meaning of a model batch may vary depending on choices you made during the model designing. 
+    The `InferenceEngine::CNNNetwork::setBatchSize` method deduces index of batch dimension relying only on the input rank. 
+    This method does not work for models with a non-zero index batch placement or models with inputs without a batch dimension. 
 
-To feed input data of a shape that is different from the model input shape, reshape the model first.
+    Batch-setting algorithm does not involve shape inference mechanism.
+    Batch of input and output shapes for all layers is set to a new batch value without layer validation.
+    It may cause both positive and negative side effects.
+ 
+    Due to the limitations described above, the current method is recommended for simple image processing models only.
 
-OpenVINO™ provides the following methods for runtime model reshaping:
 
-* **Set a new input shape** with the `InferenceEngine::CNNNetwork::reshape` method.<br>
-   The `InferenceEngine::CNNNetwork::reshape` method updates input shapes and propagates them down to the outputs of the model through all intermediate layers. 
-   You can reshape a model multiple times like in this application scheme:
-   ```
-   ReadNetwork -> reshape(input_1_shape) -> LoadNetwork -> infer(input_1)
-              \
-               -> reshape(input_2_shape) -> LoadNetwork -> infer(input_2)
-   ```
-   > **NOTES**:
-   > - Starting with the 2021.1 release, the Model Optimizer converts topologies keeping shape-calculating sub-graphs by default, which enables correct shape propagation during reshaping.
-   > - Older versions of IRs are not guaranteed to reshape successfully. Please regenerate them with the Model Optimizer of the latest version of OpenVINO™.<br>
-   > - If an ONNX model does not have a fully defined input shape and the model was imported with the ONNX importer, reshape the model before loading it to the plugin.
-* **Set a new batch dimension value** with the `InferenceEngine::CNNNetwork::setBatchSize` method.<br>     
-   The meaning of a model batch may vary depending on the model design.
-   The `InferenceEngine::CNNNetwork::setBatchSize` method deduces the index of a batch dimension based only on the input rank. 
-   This method does not work for models with a non-zero index batch placement or models with inputs without a batch dimension. 
-   The batch-setting algorithm does not involve the shape inference mechanism.
-   Batch of input and output shapes for all layers is set to a new batch value without layer validation.
-   It may cause both positive and negative side effects.
-   Due to the limitations described above, the current method is not recommended to use.
-   If you need to set a new batch size for the model, use the `CNNNetwork::reshape` method instead.
+Practically, some models are not ready to be resized. In this case, a new input shape cannot be set with the Model Optimizer or the `InferenceEngine::CNNNetwork::reshape` method.
 
-Do not use runtime reshaping methods simultaneously, especially do not call the `CNNNetwork::reshape` method after you use `InferenceEngine::CNNNetwork::setBatchSize`.
-The `InferenceEngine::CNNNetwork::setBatchSize` method causes irreversible conversion of the internal model representation into the legacy model representation.
-The method does not use nGraph for shape inference which leads to reduced reshape opportunities and may affect the performance of the model.
-
-There are other approaches to reshape the model during the stage of <a href="_docs_MO_DG_prepare_model_convert_model_Converting_Model_General.html#when_to_specify_input_shapes">IR generation</a> or [nGraph::Function creation](../nGraph_DG/build_function.md).
-
-Practically, some models are not ready to be reshaped. In this case, a new input shape cannot be set with the Model Optimizer or the `InferenceEngine::CNNNetwork::reshape` method.
-
-## Troubleshooting Reshape Errors
+## Troubleshooting Resize Errors
 
 Operation semantics may impose restrictions on input shapes of the operation. 
 Shape collision during shape propagation may be a sign that a new shape does not satisfy the restrictions. 
@@ -65,7 +42,7 @@ Examples of such operations:
 - <a href="_docs_MO_DG_prepare_model_convert_model_IR_V10_opset1.html#Reshape">`Reshape` operation</a> with a hard-coded output shape value
 - <a href="_docs_MO_DG_prepare_model_convert_model_IR_V10_opset1.html#MatMul">`MatMul` operation</a> with the `Const` second input cannot be resized by spatial dimensions due to operation semantics
 
-Model structure and logic should not change significantly after model reshaping.
+Model structure and logic should not change significantly after resizing.
 - The Global Pooling operation is commonly used to reduce output feature map of classification models output.
 Having the input of the shape [N, C, H, W], Global Pooling returns the output of the shape [N, C, 1, 1].
 Model architects usually express Global Pooling with the help of the `Pooling` operation with the fixed kernel size [H, W].
@@ -73,12 +50,12 @@ During spatial reshape, having the input of the shape [N, C, H1, W1], Pooling wi
 It breaks the classification model structure.
 For example, [publicly available Inception family models from TensorFlow*](https://github.com/tensorflow/models/tree/master/research/slim#pre-trained-models) have this issue.
 
-- Changing the model input shape may significantly affect its accuracy.
+- Resizing the model input shape may significantly affect its accuracy.
 For example, Object Detection models from TensorFlow have resizing restrictions by design. 
 To keep the model valid after the reshape, choose a new input shape that satisfies conditions listed in the `pipeline.config` file. 
 For details, refer to the <a href="_docs_MO_DG_prepare_model_convert_model_tf_specific_Convert_Object_Detection_API_Models.html#tf_od_custom_input_shape">Tensorflow Object Detection API models resizing techniques</a>.
 
-## Usage of Reshape Method <a name="usage_of_reshape_method"></a>
+## Usage of Reshape Method
 
 The primary method of the feature is `InferenceEngine::CNNNetwork::reshape`.
 It gets new input shapes and propagates it from input to output for all intermediates layers of the given network.
@@ -132,4 +109,4 @@ Shape Inference feature is used in [Smart classroom sample](@ref omz_demos_smart
 ## Extensibility
 
 Inference Engine provides a special mechanism that allows to add the support of shape inference for custom operations. 
-This mechanism is described in the [Extensibility documentation](Extensibility_DG/Intro.md)
+This mechanism is described in the [Extensibility documentation](Extensibility_DG/Intro.md).

docs/IE_DG/Tools_Overview.md

@@ -14,4 +14,4 @@ The OpenVINO™ toolkit installation includes the following tools:
 
 
 ## See Also
-* [Introduction to Inference Engine](inference_engine_intro.md)
+* [Introduction to Deep Learning Inference Engine](Introduction.md)

docs/IE_DG/inference_engine_intro.md

@@ -3,17 +3,17 @@ Introduction to Inference Engine {#openvino_docs_IE_DG_inference_engine_intro}
 
 After you have used the Model Optimizer to create an Intermediate Representation (IR), use the Inference Engine to infer the result for a given input data.
 
-Inference Engine is a set of C++ libraries providing a common API to deliver inference solutions on the platform of your choice: CPU, GPU, or VPU. Use the Inference Engine API to read the Intermediate Representation, set the input and output formats, and execute the model on devices. While the C++ libraries is the primary implementation, C libraries and Python bindings are also available.
+Inference Engine is a set of C++ libraries providing a common API to deliver inference solutions on the platform of your choice: CPU, GPU, VPU, or FPGA. Use the Inference Engine API to read the Intermediate Representation, set the input and output formats, and execute the model on devices. While the C++ libraries is the primary implementation, C libraries and Python bindings are also available.
 
 For Intel® Distribution of OpenVINO™ toolkit, Inference Engine binaries are delivered within release packages. 
 
-The open source version is available in the [OpenVINO™ toolkit GitHub repository](https://github.com/openvinotoolkit/openvino) and can be built for supported platforms using the <a href="https://github.com/openvinotoolkit/openvino/wiki/BuildingCode">Inference Engine Build Instructions</a>.    
+The open source version is available in the [OpenVINO™ toolkit GitHub repository](https://github.com/openvinotoolkit/openvino) and can be built for supported platforms using the <a href="https://github.com/openvinotoolkit/openvino/blob/master/build-instruction.md">Inference Engine Build Instructions</a>.    
 
 To learn about how to use the Inference Engine API for your application, see the [Integrating Inference Engine in Your Application](Integrate_with_customer_application_new_API.md) documentation.
 
-For complete API Reference, see the [Inference Engine API References](./api_references.html) section.
+For complete API Reference, see the [API Reference](usergroup29.html) section.
 
-Inference Engine uses a plugin architecture. Inference Engine plugin is a software component that contains complete implementation for inference on a certain Intel&reg; hardware device: CPU, GPU, VPU, etc. Each plugin implements the unified API and provides additional hardware-specific APIs.
+Inference Engine uses a plugin architecture. Inference Engine plugin is a software component that contains complete implementation for inference on a certain Intel&reg; hardware device: CPU, GPU, VPU, FPGA, etc. Each plugin implements the unified API and provides additional hardware-specific APIs.
 
 Modules in the Inference Engine component
 ---------------------------------------
@@ -53,6 +53,7 @@ For each supported target device, Inference Engine provides a plugin — a DLL/s
 | ------------- | ------------- |
 |CPU|	Intel® Xeon® with Intel® AVX2 and AVX512, Intel® Core™ Processors with Intel® AVX2, Intel® Atom® Processors with Intel® SSE |
 |GPU| Intel® Processor Graphics, including Intel® HD Graphics and Intel® Iris® Graphics
+|FPGA|	Intel® Programmable Acceleration Card with Intel® Arria® 10 GX FPGA, Intel® Vision Accelerator Design with an Intel® Arria 10 FPGA (Speed Grade 2) |
 |MYRIAD|	Intel® Neural Compute Stick 2 powered by the Intel® Movidius™ Myriad™ X|
 |GNA|	Intel&reg; Speech Enabling Developer Kit, Amazon Alexa* Premium Far-Field Developer Kit, Intel&reg; Pentium&reg; Silver J5005 Processor, Intel&reg; Pentium&reg; Silver N5000 Processor, Intel&reg; Celeron&reg; J4005 Processor, Intel&reg; Celeron&reg; J4105 Processor, Intel&reg; Celeron&reg; Processor N4100, Intel&reg; Celeron&reg; Processor N4000, Intel&reg; Core&trade; i3-8121U Processor, Intel&reg; Core&trade; i7-1065G7 Processor, Intel&reg; Core&trade; i7-1060G7 Processor, Intel&reg; Core&trade; i5-1035G4 Processor, Intel&reg; Core&trade; i5-1035G7 Processor, Intel&reg; Core&trade; i5-1035G1 Processor, Intel&reg; Core&trade; i5-1030G7 Processor, Intel&reg; Core&trade; i5-1030G4 Processor, Intel&reg; Core&trade; i3-1005G1 Processor, Intel&reg; Core&trade; i3-1000G1 Processor, Intel&reg; Core&trade; i3-1000G4 Processor
 |HETERO|Automatic splitting of a network inference between several devices (for example if a device doesn't support certain layers|
@@ -64,6 +65,7 @@ The table below shows the plugin libraries and additional dependencies for Linux
 |--------|------------------------|-------------------------------------------------|--------------------------|--------------------------------------------------------------------------------------------------------|
 | CPU    | `libMKLDNNPlugin.so`   | `libinference_engine_lp_transformations.so` | `MKLDNNPlugin.dll`       | `inference_engine_lp_transformations.dll`    |
 | GPU    | `libclDNNPlugin.so`    | `libinference_engine_lp_transformations.so`, `libOpenCL.so`                                  | `clDNNPlugin.dll`        | `OpenCL.dll`, `inference_engine_lp_transformations.dll`                                                                                           |
+| FPGA   | `libdliaPlugin.so`     | `libdla_compiler_core.so`, `libdla_runtime_core.so`, `libcrypto.so`, `libalteracl.so`, `liblpsolve5525.so`, `libprotobuf.so`, `libacl_emulator_kernel_rt.so` | `dliaPlugin.dll`         | `dla_compiler_core.dll`, `dla_runtime_core.dll`, `crypto.dll`, `alteracl.dll`, `lpsolve5525.dll`, `protobuf.dll`, `acl_emulator_kernel_rt.dll`
 | MYRIAD | `libmyriadPlugin.so`   | `libusb.so`, `libinference_engine_lp_transformations.so`                                 | `myriadPlugin.dll`       | `usb.dll`, `inference_engine_lp_transformations.dll`                                                                                        |
 | HDDL   | `libHDDLPlugin.so`     | `libbsl.so`, `libhddlapi.so`, `libmvnc-hddl.so`, `libinference_engine_lp_transformations.so`| `HDDLPlugin.dll`         | `bsl.dll`, `hddlapi.dll`, `json-c.dll`, `libcrypto-1_1-x64.dll`, `libssl-1_1-x64.dll`, `mvnc-hddl.dll`, `inference_engine_lp_transformations.dll` |
 | GNA    | `libGNAPlugin.so`      | `libgna.so`, `libinference_engine_lp_transformations.so`                                 | `GNAPlugin.dll`          | `gna.dll`, `inference_engine_lp_transformations.dll`                                                                                              |

docs/IE_DG/nGraphTutorial.md

@@ -0,0 +1,64 @@
+# Build a Model with nGraph Library {#openvino_docs_IE_DG_nGraphTutorial}
+
+This section illustrates how to construct an nGraph function 
+composed of operations from the `opset3` namespace. Once created, 
+it can wrap into a `CNNNetwork`, creating utility for data scientists 
+or app developers to define a deep-learning model in a neutral way
+that does not depend on existing Deep Learning (DL) frameworks.
+
+Operation Set `opsetX` integrates a list of nGraph pre-compiled operations that work
+for this purpose. In other words, `opsetX` defines a set of operations for building a graph.
+
+For a complete list of operation sets supported by Inference Engine, see [Available Operations Sets](../ops/opset.md).
+
+To add custom nGraph operations to an existing `CNNNetwork`, see 
+the [Add Custom nGraph Operations](Extensibility_DG/Intro.md) document.
+
+Now that you can build graphs with anything from the `opset3` definition, some 
+parameters for shape-relevant (or shape-specific) inputs can be added. The 
+following code prepares a graph for shape-relevant parameters. 
+
+> **NOTE**: `validate_nodes_and_infer_types(ops)` must be included for partial shape inference. 
+
+```cpp
+#include "ngraph/opsets/opset.hpp"
+#include "ngraph/opsets/opset3.hpp"
+
+using namespace std;
+using namespace ngraph;
+
+auto arg0 = make_shared<opset3::Parameter>(element::f32, Shape{7});
+auto arg1 = make_shared<opset3::Parameter>(element::f32, Shape{7});
+// Create an 'Add' operation with two inputs 'arg0' and 'arg1'
+auto add0 = make_shared<opset3::Add>(arg0, arg1);
+auto abs0 = make_shared<opset3::Abs>(add0);
+// Create a node whose inputs/attributes will be specified later
+auto acos0 = make_shared<opset3::Acos>();
+// Create a node using opset factories
+auto add1 = shared_ptr<Node>(get_opset3().create("Add"));
+// Set inputs to nodes explicitly
+acos0->set_argument(0, add0);
+add1->set_argument(0, acos0);
+add1->set_argument(1, abs0);
+
+// Run shape inference on the nodes
+NodeVector ops{arg0, arg1, add0, abs0, acos0, add1};
+validate_nodes_and_infer_types(ops);
+
+// Create a graph with one output (add1) and four inputs (arg0, arg1)
+auto ng_function = make_shared<Function>(OutputVector{add1}, ParameterVector{arg0, arg1});
+
+```
+
+To wrap it into a CNNNetwork, use: 
+```cpp
+CNNNetwork net (ng_function);
+```
+
+## See Also
+
+* [Available Operation Sets](../ops/opset.md)
+* [Operation Set `opset1` Specification](../ops/opset1.md)
+* [Operation Set `opset2` Specification](../ops/opset2.md)
+* [Operation Set `opset3` Specification](../ops/opset3.md)
+* [Inference Engine Extensibility Developer Guide](Extensibility_DG/Intro.md)

docs/IE_DG/nGraph_Flow.md

@@ -0,0 +1,142 @@
+# Introduction to nGraph Flow in Inference Engine {#openvino_docs_IE_DG_nGraph_Flow}
+
+## New Run-Time Intermediate Representation (IR): nGraph
+
+Starting from the OpenVINO™ release 2020.1, the Inference Engine integrates the 
+nGraph Core.
+That implies that the Inference Engine uses a new way to represent a model in run time underneath of 
+the conventional `CNNNetwork` API, which is an instance of `ngraph::Function`.
+
+Besides the representation update, nGraph integration resulted in the following changes and new features:
+
+1. New operations sets. When operations from the nGraph Core were combined with conventional layers 
+from `CNNNetwork`, there were created a [new sets of operations called `opset1`, `opset2` and etc.](../ops/opset.md), 
+which covered both interfaces except several not very important cases.
+Operations from `opset3` are generated by the Model Optimizer and are accepted in the Inference Engine.
+
+2. New version approach that attaches a version to each operation rather than to the entire IR file format. 
+IR is still versioned but has a different meaning. For details, see [Deep Learning Network Intermediate Representation and Operation Sets in OpenVINO™](../MO_DG/IR_and_opsets.md).
+
+3. Creating models in run-time without loading IR from an xml/binary file. You can enable it by creating
+`ngraph::Function` passing it to `CNNNetwork`.
+
+4. Run-time reshape capability and constant folding are implemented through the nGraph code for more operations compared to previous releases.
+As a result, more models can be reshaped. For details, see the [dedicated guide about the reshape capability](ShapeInference.md).
+
+5. Loading model from ONNX format without converting it to the Inference Engine IR.
+
+The conventional flow that is not based on nGraph is still available.
+The complete picture of co-existence of legacy and new flows is presented below.
+The rest of the document describes the coexistence of legacy and new flows showed in the picture below:
+
+![](img/TopLevelNGraphFlow.png)
+
+
+## Read the Intermediate Representation to `CNNNetwork`
+
+As the new operation set is introduced, the Model Optimizer generates the IR version 10 using the new operations by default.
+Each layer generated in the IR has a semantics matching to the corresponding operation from the nGraph namespace `opset3`.
+The IR version 10 automatically triggers the nGraph flow inside the Inference Engine.
+When such IR is read in an application, the Inference Engine IR reader produces `CNNNetwork` that encapsulates the `ngraph::Function` instance underneath.
+Thus the OpenVINO IR becomes a new serialization format for the nGraph IR, and it can be deserialized reading the `CNNNetwork`.
+
+> **IMPORTANT**: Conventional interfaces are used (`CNNNetwork`, the reader), so no changes required in most applications.
+
+> **NOTE**: While you still can use old APIs, there is an independent process of continuous improvements in the Inference Engine API.
+> For example, the Core::Read API is recommended to use instead of `CNNNetworkReader`.
+> These changes are independent of nGraph integration and do not enable or disable new features.
+
+Interpretation of the IR version 10 differs from the old IR version.
+Besides having a different operations set, the IR version 10 ignores the shapes and data types assigned to the ports in an XML file.
+Both shapes and types are reinferred while loading to the Inference Engine using the nGraph shape and type propagation function that is a part of each nGraph operation.
+
+### Legacy IR Versions
+
+You can read old versions of the IR in the Inference Engine.
+Each version below or equal to 7 is treated as an old one.
+When the Inference Engine reader reads an old version of the IR, it does not use the nGraph representation.
+There is no way to activate nGraph flow with an old IR version.
+The rest of this document is not applied in this case.
+
+Model Optimizer generates the IR version 10 by default, and there is the command line key `--generate_deprecated_IR_V7` which switches generation to the legacy IR version 7.
+It is useful when the new nGraph flow does not work for some reason.
+
+## Build a Model in the Application
+
+Alternative method to feed the Inference Engine with a model is to create the model in the run time.
+It is achieved by creation of the `ngraph::Function` construction using nGraph operation classes and optionally user-defined operations.
+For details, see [Add Custom nGraph Operations](Extensibility_DG/AddingNGraphOps.md) and [examples](nGraphTutorial.md).
+At this stage, the code is completely independent of the rest of the Inference Engine code and can be built separately.
+After you construct an instance of `ngraph::Function`, you can use it to create `CNNNetwork` by passing it to the new constructor for this class.
+
+Initializing `CNNNetwork` from the nGraph Function means encapsulating the object and not converting it to a conventional representation.
+Going to low-level details, technically it is achieved by using another class for the `CNNNetwork` internals.
+The old representation that is used for former versions of IR before version 10 uses `CNNNetworkImpl`.
+The new representation that is built around nGraph uses `CNNNetworkNGraphImpl`.
+
+![](img/NewAndOldCNNNetworkImpl.png)
+
+## Automatic Conversion to the Old Representation
+
+The old representation is still required in the cases listed below.
+When old representation is required, the conversion from the `ngraph::Function` to the old representation is called automatically.
+The following methods lead to the automatic conversion:
+
+1. Using the old API, which is expected to produce an old representation. Guaranteed to be read-only. Once you call such a method, the original nGraph representation is preserved and continues to be used in the successive calls.
+    
+    1.1. `CNNNetwork::serialize`. Dumps the old representation after automatically called conversion. Cannot be used to dump IR V10. For details, see [Graph Debug Capabilities](Graph_debug_capabilities.md).
+
+2. Calling `CNNNetwork` methods that modify the model. After that nGraph representation is lost and cannot be used afterwards.
+
+    1.1. `CNNNetwork::addLayer`
+
+    1.2. CNNNetwork::setBatchSize. Still implemented through old logic for backward compatibility without using nGraph capabilities.
+    For details, see [Using Shape Inference](ShapeInference.md).
+
+3. Using methods that return objects inside an old representation.
+Using these methods does not mean modification of the model, but you are not limited by the API to make read-only changes. 
+These methods should be used in the read-only mode with respect to a model representation.
+If the model is changed, for example attribute of some layer is changed or layers are reconnected, the modification is lost whenever any method that uses nGraph is called, including methods inside plugins like CNNNetwork::reshape.
+It is hard to predict whether the nGraph function is used in a plugin or other methods of CNNNetworks, so modifying a network using the following methods is *strongly not recommended*.
+This is an important limitation that is introduced for the old API calls listed below:
+
+    1.1. `Data::getInputTo`
+    
+    1.2. `Data::getCreatorLayer`
+    
+    1.3. `CNNNetwork::getLayerByName`
+    
+    1.4. Iterating over `CNNLayer` objects in `CNNNetwork`: `CNNNetwork::begin`, `details::CNNNetworkIterator` class.
+
+4. Using a conventional plugin that accepts the old representation only.
+
+Though the conversion is always a one-way process, which means there is no method to convert back, there are important caveats.
+
+In the cases [1] and [3], both representations are held underneath and you should use the old representation in the read-only mode only from the caller side.
+It is hard to track from the Inference Engine side whether the API is used in the read-only mode or for modification of the model.
+
+That is why when using potentially modifying methods listed in section [3] above, you should not modify the model via those methods.
+Use a direct manipulation of the nGraph function instead.
+
+## Conversion Function
+
+Inference Engine implements the conversion function that is used when the nGraph function is transformed to the old `CNNNetworkImpl` representation.
+This conversion function is hidden and you cannot call it directly from the application.
+Nevertheless, it is an important component of the model transformation pipeline in the Inference Engine.
+Some issues of models may be caught during the conversion process in this function.
+Exceptions are thrown in this function, and you should know what this function does to find a root cause.
+
+The conversion function performs the following steps:
+
+1. Convert and decompose some operations as the first step of the nGraph function preparation for optimization.
+Reduce operation set to easily optimize it at the next stages.
+For example, decomposing of BatchNormInference happens at this stage.
+
+2. Optimizing transformations that usually happen in the Model Optimizer are called here, because the nGraph function is not always read from an already optimized IR.
+
+3. Changing operation set from `opsetX` to legacy layer semantics described in the [Legacy Layers Catalog](../MO_DG/prepare_model/convert_model/Legacy_IR_Layers_Catalog_Spec.md).
+The model is still represented as the nGraph function at this stage, but the operation set is completely different.
+
+4. One-to-one conversion of nGraph representation to the corresponding `CNNNetworkImpl` without changing its semantics.
+You can see the result of the conversion by calling the `CNNNetwork::serialize` method, which produces legacy IR semantics, which is not nGraph-based even if it is applied to `CNNNetwork` constructed from the nGraph Function.
+It may help in debugging, see [Graph Debug Capabilities](Graph_debug_capabilities.md) to view all options for dumping new and old IR representations.

docs/IE_DG/protecting_model_guide.md

@@ -63,9 +63,9 @@ CNNNetwork network = core.ReadNetwork(strModel, make_shared_blob<uint8_t>({Preci
 
 - Intel® Distribution of OpenVINO™ toolkit home page: [https://software.intel.com/en-us/openvino-toolkit](https://software.intel.com/en-us/openvino-toolkit)
 - OpenVINO™ toolkit online documentation: [https://docs.openvinotoolkit.org](https://docs.openvinotoolkit.org)
-- Model Optimizer Developer Guide: [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
-- Inference Engine Developer Guide: [Inference Engine Developer Guide](Deep_Learning_Inference_Engine_DevGuide.md)
-- For more information on Sample Applications, see the [Inference Engine Samples Overview](Samples_Overview.md)
+- Model Optimizer Developer Guide: [Model Optimizer Developer Guide](https://docs.openvinotoolkit.org/latest/_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html)
+- Inference Engine Developer Guide: [Inference Engine Developer Guide](https://docs.openvinotoolkit.org/latest/_docs_IE_DG_Deep_Learning_Inference_Engine_DevGuide.html)
+- For more information on Sample Applications, see the [Inference Engine Samples Overview](https://docs.openvinotoolkit.org/latest/_docs_IE_DG_Samples_Overview.html)
 - For information on a set of pre-trained models, see the [Overview of OpenVINO™ Toolkit Pre-Trained Models](@ref omz_models_intel_index)
 - For information on Inference Engine Tutorials, see the [Inference Tutorials](https://github.com/intel-iot-devkit/inference-tutorials-generic)
 - For IoT Libraries and Code Samples see the [Intel® IoT Developer Kit](https://github.com/intel-iot-devkit).

docs/IE_DG/supported_plugins/CPU.md

@@ -14,8 +14,8 @@ OpenVINO™ toolkit is officially supported and validated on the following platf
 
 | Host              | OS (64-bit)                              |
 | :---              | :---                                     |
-| Development       | Ubuntu* 18.04, CentOS* 7.5, MS Windows* 10 |
-| Target            | Ubuntu* 18.04, CentOS* 7.5, MS Windows* 10 |
+| Development       | Ubuntu* 16.04/CentOS* 7.4/MS Windows* 10 |
+| Target            | Ubuntu* 16.04/CentOS* 7.4/MS Windows* 10 |
 
 The CPU Plugin supports inference on Intel® Xeon® with Intel® Advanced Vector Extensions 2 (Intel® AVX2), Intel® Advanced Vector Extensions 512 (Intel® AVX-512), and AVX512_BF16, Intel® Core™
 Processors with Intel® AVX2, Intel Atom® Processors with Intel® Streaming SIMD Extensions (Intel® SSE).
@@ -114,11 +114,9 @@ CPU-specific settings:
 | :---                        | :---                  | :---               | :--- |
 | KEY_CPU_THREADS_NUM         | positive integer values| 0                 | Specifies the number of threads that CPU plugin should use for inference. Zero (default) means using all (logical) cores|
 | KEY_CPU_BIND_THREAD         | YES/NUMA/NO           | YES                | Binds inference threads to CPU cores. 'YES' (default) binding option maps threads to cores - this works best for static/synthetic scenarios like benchmarks. The 'NUMA' binding is more relaxed, binding inference threads only to NUMA nodes, leaving further scheduling to specific cores to the OS. This option might perform better in the real-life/contended scenarios. Note that for the latency-oriented cases (single execution stream, see below) both YES and NUMA options limit number of inference threads to the number of hardware cores (ignoring hyper-threading) on the multi-socket machines. |
-| KEY_CPU_THROUGHPUT_STREAMS  | KEY_CPU_THROUGHPUT_NUMA, KEY_CPU_THROUGHPUT_AUTO, or positive integer values| 1 | Specifies number of CPU "execution" streams for the throughput mode. Upper bound for the number of inference requests that can be executed simultaneously. All available CPU cores are evenly distributed between the streams. The default value is 1, which implies latency-oriented behavior with all available cores processing requests one by one.<br>KEY_CPU_THROUGHPUT_NUMA creates as many streams as needed to accommodate NUMA and avoid associated penalties.<br>KEY_CPU_THROUGHPUT_AUTO creates bare minimum of streams to improve the performance; this is the most portable option if you don't know how many cores your target machine has (and what would be the optimal number of streams). Note that your application should provide enough parallel slack (for example, run many inference requests) to leverage the throughput mode. <br> Non-negative integer value creates the requested number of streams. If a number of streams is 0, no internal streams are created and user threads are interpreted as stream master threads.|
+| KEY_CPU_THROUGHPUT_STREAMS  | KEY_CPU_THROUGHPUT_NUMA, KEY_CPU_THROUGHPUT_AUTO, or positive integer values| 1 | Specifies number of CPU "execution" streams for the throughput mode. Upper bound for the number of inference requests that can be executed simultaneously. All available CPU cores are evenly distributed between the streams. The default value is 1, which implies latency-oriented behavior with all available cores processing requests one by one.<br>KEY_CPU_THROUGHPUT_NUMA creates as many streams as needed to accommodate NUMA and avoid associated penalties.<br>KEY_CPU_THROUGHPUT_AUTO creates bare minimum of streams to improve the performance; this is the most portable option if you don't know how many cores your target machine has (and what would be the optimal number of streams). Note that your application should provide enough parallel slack (for example, run many inference requests) to leverage the throughput mode. <br> A positive integer value creates the requested number of streams. |
 | KEY_ENFORCE_BF16            | YES/NO| YES | The name for setting to execute in bfloat16 precision whenever it is possible. This option lets plugin know to downscale the precision where it sees performance benefits from bfloat16 execution. Such option does not guarantee accuracy of the network, you need to verify the accuracy in this mode separately, based on performance and accuracy results. It should be your decision whether to use this option or not. |
 
-> **NOTE**: To disable all internal threading, use the following set of configuration parameters: `KEY_CPU_THROUGHPUT_STREAMS=0`, `KEY_CPU_THREADS_NUM=1`, `KEY_CPU_BIND_THREAD=NO`.
-
 ## See Also
 * [Supported Devices](Supported_Devices.md)
 

docs/IE_DG/supported_plugins/FPGA.md

@@ -19,4 +19,294 @@ Intel will be transitioning to the next-generation programmable deep-learning so
 
 Intel® Distribution of OpenVINO™ toolkit 2020.3.X LTS release will continue to support Intel® Vision Accelerator Design with an Intel® Arria® 10 FPGA and the Intel® Programmable Acceleration Card with Intel® Arria® 10 GX FPGA. For questions about next-generation programmable deep-learning solutions based on FPGAs, please talk to your sales representative or contact us to get the latest FPGA updates.
 
-For documentation for the FPGA plugin available in previous releases of Intel® Distribution of OpenVINO™ toolkit with FPGA Support, see documentation for the [2020.4 version](https://docs.openvinotoolkit.org/2020.4/openvino_docs_IE_DG_supported_plugins_FPGA.html) and lower.
+## Introducing FPGA Plugin
+
+The FPGA plugin provides an opportunity for high performance scoring of neural networks on Intel® FPGA devices.
+
+> **NOTE**: Before using the FPGA plugin, ensure that you have installed and configured either the Intel® Vision Accelerator Design with an Intel® Arria® 10 FPGA (Speed Grade 2) or the Intel® Programmable Acceleration Card with Intel® Arria® 10 GX FPGA. For installation and configuration details, see [FPGA installation](Supported_Devices.md).
+
+## Heterogeneous Execution
+
+When your topology contains layers that are not supported by the Intel® FPGA plugin, use [Heterogeneous plugin](HETERO.md) with dedicated fallback device.
+
+If a network has layers that are not supported in the Intel® FPGA plugin or in a fallback plugin, you can implement a custom layer on the CPU/GPU and use the [Extensibility mechanism](../Extensibility_DG/Intro.md).
+In addition to adding custom kernels, you must still point to the CPU plugin or the GPU plugin as fallback devices for heterogeneous plugin.
+
+## Supported Networks
+
+The following network topologies are supported in heterogeneous mode, running on FPGA with fallback to CPU or GPU devices.
+
+> **IMPORTANT**: Use only bitstreams from the current version of the OpenVINO toolkit. Bitstreams from older versions of the OpenVINO toolkit are incompatible with later versions of the OpenVINO toolkit. For example, you cannot use the `1-0-1_A10DK_FP16_Generic` bitstream, when the OpenVINO toolkit supports the `2019R2_PL2_FP16_InceptionV1_SqueezeNet_VGG_YoloV3.aocx` bitstream.
+
+
+| Network                              | Bitstreams (Intel® Vision Accelerator Design with an Intel® Arria® 10 FPGA (Speed Grade 2)) | Bitstreams (Intel® Programmable Acceleration Card with Intel® Arria® 10 GX FPGA) |
+|:-------------------------------------|:-------------------------------------------------------------------|:---------------------------------------------------------------------------------------------|
+| AlexNet                              | 2020-4_PL2_FP16_AlexNet_GoogleNet_SSD300_Generic, 2020-4_PL2_FP11_AlexNet_GoogleNet_Generic | 2020-4_RC_FP16_AlexNet_GoogleNet_Generic, 2020-4_RC_FP11_AlexNet_GoogleNet_Generic |
+| GoogleNet v1                         | 2020-4_PL2_FP16_AlexNet_GoogleNet_SSD300_Generic, 2020-4_PL2_FP11_AlexNet_GoogleNet_Generic | 2020-4_RC_FP16_AlexNet_GoogleNet_Generic, 2020-4_RC_FP11_AlexNet_GoogleNet_Generic |
+| VGG-16                               | 2020-4_PL2_FP16_SqueezeNet_TinyYolo_VGG, 2020-4_PL2_FP11_InceptionV1_ResNet_VGG | 2020-4_RC_FP16_InceptionV1_SqueezeNet_TinyYolo_VGG, 2020-4_RC_FP16_ResNet_TinyYolo_VGG |
+| VGG-19                               | 2020-4_PL2_FP16_SqueezeNet_TinyYolo_VGG, 2020-4_PL2_FP11_InceptionV1_ResNet_VGG | 2020-4_RC_FP16_InceptionV1_SqueezeNet_TinyYolo_VGG, 2020-4_RC_FP16_ResNet_TinyYolo_VGG |
+| SqueezeNet v 1.0                     | 2020-4_PL2_FP16_SqueezeNet_TinyYolo_VGG, 2020-4_PL2_FP11_SqueezeNet | 2020-4_RC_FP16_InceptionV1_SqueezeNet_YoloV3, 2020-4_RC_FP16_InceptionV1_SqueezeNet_YoloV3 |
+| SqueezeNet v 1.1                     | 2020-4_PL2_FP16_SqueezeNet_TinyYolo_VGG, 2020-4_PL2_FP11_SqueezeNet | 2020-4_RC_FP16_InceptionV1_SqueezeNet_YoloV3, 2020-4_RC_FP16_InceptionV1_SqueezeNet_YoloV3 |
+| ResNet-18                            | 2020-4_PL2_FP16_ResNet_YoloV3, 2020-4_PL2_FP11_InceptionV1_ResNet_VGG | 2020-4_RC_FP16_ResNet_YoloV3, 2020-4_RC_FP16_ResNet_TinyYolo_VGG |
+| ResNet-50                            | 2020-4_PL2_FP16_ResNet_YoloV3, 2020-4_PL2_FP11_InceptionV1_ResNet_VGG | 2020-4_RC_FP16_ResNet_YoloV3, 2020-4_RC_FP16_ResNet_TinyYolo_VGG |
+| ResNet-101                           | 2020-4_PL2_FP16_ResNet_YoloV3, 2020-4_PL2_FP11_InceptionV1_ResNet_VGG | 2020-4_RC_FP16_ResNet_YoloV3, 2020-4_RC_FP16_ResNet_TinyYolo_VGG |
+| ResNet-152                           | 2020-4_PL2_FP16_ResNet_YoloV3, 2020-4_PL2_FP11_InceptionV1_ResNet_VGG | 2020-4_RC_FP16_ResNet_YoloV3, 2020-4_RC_FP16_ResNet_TinyYolo_VGG |
+| MobileNet (Caffe)                    | 2020-4_PL2_FP16_MobileNet_Clamp, 2020-4_PL2_FP11_MobileNet_Clamp | 2020-4_RC_FP16_MobileNet_Clamp, 2020-4_RC_FP11_MobileNet_Clamp |
+| MobileNet (TensorFlow)               | 2020-4_PL2_FP16_MobileNet_Clamp, 2020-4_PL2_FP11_MobileNet_Clamp | 2020-4_RC_FP16_MobileNet_Clamp, 2020-4_RC_FP11_MobileNet_Clamp|
+| SqueezeNet-based variant of the SSD* | 2020-4_PL2_FP16_SqueezeNet_TinyYolo_VGG, 2020-4_PL2_FP11_SqueezeNet | 2020-4_RC_FP16_InceptionV1_SqueezeNet_TinyYolo_VGG, 2020-4_RC_FP16_InceptionV1_SqueezeNet_YoloV3 |
+| ResNet-based variant of SSD          | 2020-4_PL2_FP16_ResNet_YoloV3, 2020-4_PL2_FP11_InceptionV1_ResNet_VGG | 2020-4_RC_FP16_ResNet_YoloV3, 2020-4_RC_FP16_ResNet_TinyYolo_VGG |
+| RMNet                                | 2020-4_PL2_FP16_RMNet, 2020-4_PL2_FP11_RMNet | 2020-4_RC_FP16_RMNet, 2020-4_RC_FP11_RMNet |
+| Yolo v3                              | 2020-4_PL2_FP16_ResNet_YoloV3, 2020-4_PL2_FP11_YoloV3_ELU | 2020-4_RC_FP16_ResNet_YoloV3, 2020-4_RC_FP16_InceptionV1_SqueezeNet_YoloV3 |
+
+
+In addition to the list above, arbitrary topologies having big continues subgraphs consisting of layers supported by FPGA plugin are recommended to be executed on FPGA plugin.
+
+## Bitstreams that are Optimal to Use with the Intel's Pre-Trained Models
+
+The table below provides you with a list of Intel® Vision Accelerator Design with an Intel® Arria® 10 FPGA (Speed Grade 2) bitstreams that are optimal to use for the Intel's pre-trained models.  
+
+<details>
+  <summary><strong>Click to expand/collapse the table</strong></summary>
+
+| Model Name | FP11 Bitstreams | FP16 Bitstreams |
+| :---       | :---            | :---            |
+| action-recognition-0001-decoder | 2020-3_PL2_FP11_AlexNet_GoogleNet_Generic.aocx | 2020-3_PL2_FP16_SwishExcitation.aocx |
+| action-recognition-0001-encoder | 2020-3_PL2_FP11_InceptionV1_ResNet_VGG.aocx | 2020-3_PL2_FP16_ResNet_YoloV3.aocx |
+| age-gender-recognition-retail-0013 | 2020-3_PL2_FP11_AlexNet_GoogleNet_Generic.aocx | 2020-3_PL2_FP16_MobileNet_Clamp.aocx |
+| asl-recognition-0004 | 2020-3_PL2_FP11_AlexNet_GoogleNet_Generic.aocx | 2020-3_PL2_FP16_AlexNet_GoogleNet_SSD300_Generic.aocx |
+| driver-action-recognition-adas-0002-decoder | 2020-3_PL2_FP11_AlexNet_GoogleNet_Generic.aocx | 2020-3_PL2_FP16_MobileNet_Clamp.aocx |
+| driver-action-recognition-adas-0002-encoder | 2020-3_PL2_FP11_YoloV3_ELU.aocx | 2020-3_PL2_FP16_SwishExcitation.aocx |
+| emotions-recognition-retail-0003 | 2020-3_PL2_FP11_AlexNet_GoogleNet_Generic.aocx | 2020-3_PL2_FP16_SqueezeNet_TinyYolo_VGG.aocx |
+| face-detection-0100 | 2020-3_PL2_FP11_MobileNet_Clamp.aocx | 2020-3_PL2_FP16_MobileNet_Clamp.aocx |
+| face-detection-0102 | 2020-3_PL2_FP11_MobileNet_Clamp.aocx | 2020-3_PL2_FP16_MobileNet_Clamp.aocx |
+| face-detection-0104 | 2020-3_PL2_FP11_MobileNet_Clamp.aocx | 2020-3_PL2_FP16_MobileNet_Clamp.aocx |
+| face-detection-0105 | 2020-3_PL2_FP11_MobileNet_Clamp.aocx | 2020-3_PL2_FP16_MobileNet_Clamp.aocx |
+| face-detection-0106 | 2020-3_PL2_FP11_InceptionV1_ResNet_VGG.aocx | 2020-3_PL2_FP16_ResNet_YoloV3.aocx |
+| face-detection-adas-0001 | 2020-3_PL2_FP11_YoloV3_ELU.aocx | 2020-3_PL2_FP16_SwishExcitation.aocx |
+| face-detection-adas-binary-0001 | 2020-3_PL2_FP11_AlexNet_GoogleNet_Generic.aocx | 2020-3_PL2_FP16_AlexNet_GoogleNet_SSD300_Generic.aocx |
+| face-detection-retail-0004 | 2020-3_PL2_FP11_TinyYolo_SSD300.aocx | 2020-3_PL2_FP16_AlexNet_GoogleNet_SSD300_Generic.aocx |
+| face-detection-retail-0005 | 2020-3_PL2_FP11_YoloV3_ELU.aocx | 2020-3_PL2_FP16_MobileNet_Clamp.aocx |
+| face-reidentification-retail-0095 | 2020-3_PL2_FP11_SqueezeNet.aocx | 2020-3_PL2_FP16_MobileNet_Clamp.aocx |
+| facial-landmarks-35-adas-0002 | 2020-3_PL2_FP11_AlexNet_GoogleNet_Generic.aocx | 2020-3_PL2_FP16_MobileNet_Clamp.aocx |
+| faster-rcnn-resnet101-coco-sparse-60-0001 | 2020-3_PL2_FP11_InceptionV1_ResNet_VGG.aocx | 2020-3_PL2_FP16_SwishExcitation.aocx |
+| gaze-estimation-adas-0002 | 2020-3_PL2_FP11_SqueezeNet.aocx | 2020-3_PL2_FP16_AlexNet_GoogleNet_SSD300_Generic.aocx |
+| handwritten-japanese-recognition-0001 | 2020-3_PL2_FP11_InceptionV1_ResNet_VGG.aocx | 2020-3_PL2_FP16_ResNet_YoloV3.aocx |
+| handwritten-score-recognition-0003 | 2020-3_PL2_FP11_InceptionV1_ResNet_VGG.aocx | 2020-3_PL2_FP16_SqueezeNet_TinyYolo_VGG.aocx |
+| head-pose-estimation-adas-0001 | 2020-3_PL2_FP11_SqueezeNet.aocx | 2020-3_PL2_FP16_AlexNet_GoogleNet_SSD300_Generic.aocx |
+| human-pose-estimation-0001 | 2020-3_PL2_FP11_YoloV3_ELU.aocx | 2020-3_PL2_FP16_SwishExcitation.aocx |
+| icnet-camvid-ava-0001 | 2020-3_PL2_FP11_AlexNet_GoogleNet_Generic.aocx | 2020-3_PL2_FP16_AlexNet_GoogleNet_SSD300_Generic.aocx |
+| icnet-camvid-ava-sparse-30-0001 | 2020-3_PL2_FP11_SqueezeNet.aocx | 2020-3_PL2_FP16_SwishExcitation.aocx |
+| icnet-camvid-ava-sparse-60-0001 | 2020-3_PL2_FP11_SqueezeNet.aocx | 2020-3_PL2_FP16_SwishExcitation.aocx |
+| image-retrieval-0001 | 2020-3_PL2_FP11_MobileNet_Clamp.aocx | 2020-3_PL2_FP16_MobileNet_Clamp.aocx |
+| instance-segmentation-security-0010 | 2020-3_PL2_FP11_InceptionV1_ResNet_VGG.aocx | 2020-3_PL2_FP16_SqueezeNet_TinyYolo_VGG.aocx |
+| instance-segmentation-security-0050 | 2020-3_PL2_FP11_InceptionV1_ResNet_VGG.aocx | 2020-3_PL2_FP16_ResNet_YoloV3.aocx |
+| instance-segmentation-security-0083 | 2020-3_PL2_FP11_AlexNet_GoogleNet_Generic.aocx | 2020-3_PL2_FP16_AlexNet_GoogleNet_SSD300_Generic.aocx |
+| instance-segmentation-security-1025 | 2020-3_PL2_FP11_InceptionV1_ResNet_VGG.aocx | 2020-3_PL2_FP16_AlexNet_GoogleNet_SSD300_Generic.aocx |
+| landmarks-regression-retail-0009 | 2020-3_PL2_FP11_SqueezeNet.aocx | 2020-3_PL2_FP16_AlexNet_GoogleNet_SSD300_Generic.aocx |
+| license-plate-recognition-barrier-0001 | 2020-3_PL2_FP11_SqueezeNet.aocx | 2020-3_PL2_FP16_SqueezeNet_TinyYolo_VGG.aocx |
+| pedestrian-and-vehicle-detector-adas-0001 | 2020-3_PL2_FP11_YoloV3_ELU.aocx | 2020-3_PL2_FP16_SwishExcitation.aocx |
+| pedestrian-detection-adas-0002 | 2020-3_PL2_FP11_YoloV3_ELU.aocx | 2020-3_PL2_FP16_SwishExcitation.aocx |
+| pedestrian-detection-adas-binary-0001 | 2020-3_PL2_FP11_AlexNet_GoogleNet_Generic.aocx | 2020-3_PL2_FP16_AlexNet_GoogleNet_SSD300_Generic.aocx |
+| person-attributes-recognition-crossroad-0230 | 2020-3_PL2_FP11_SqueezeNet.aocx | 2020-3_PL2_FP16_MobileNet_Clamp.aocx |
+| person-detection-action-recognition-0005 | 2020-3_PL2_FP11_YoloV3_ELU.aocx | 2020-3_PL2_FP16_MobileNet_Clamp.aocx |
+| person-detection-action-recognition-0006 | 2020-3_PL2_FP11_YoloV3_ELU.aocx | 2020-3_PL2_FP16_MobileNet_Clamp.aocx |
+| person-detection-action-recognition-teacher-0002 | 2020-3_PL2_FP11_YoloV3_ELU.aocx | 2020-3_PL2_FP16_MobileNet_Clamp.aocx |
+| person-detection-asl-0001 | 2020-3_PL2_FP11_AlexNet_GoogleNet_Generic.aocx | 2020-3_PL2_FP16_AlexNet_GoogleNet_SSD300_Generic.aocx |
+| person-detection-raisinghand-recognition-0001 | 2020-3_PL2_FP11_YoloV3_ELU.aocx | 2020-3_PL2_FP16_MobileNet_Clamp.aocx |
+| person-detection-retail-0002 | 2020-3_PL2_FP11_InceptionV1_ResNet_VGG.aocx | 2020-3_PL2_FP16_MobileNet_Clamp.aocx |
+| person-detection-retail-0013 | 2020-3_PL2_FP11_MobileNet_Clamp.aocx | 2020-3_PL2_FP16_MobileNet_Clamp.aocx |
+| person-reidentification-retail-0031 | 2020-3_PL2_FP11_YoloV3_ELU.aocx | 2020-3_PL2_FP16_ELU.aocx |
+| person-reidentification-retail-0248 | 2020-3_PL2_FP11_MobileNet_Clamp.aocx | 2020-3_PL2_FP16_MobileNet_Clamp.aocx |
+| person-reidentification-retail-0249 | 2020-3_PL2_FP11_MobileNet_Clamp.aocx | 2020-3_PL2_FP16_MobileNet_Clamp.aocx |
+| person-reidentification-retail-0300 | 2020-3_PL2_FP11_InceptionV1_ResNet_VGG.aocx | 2020-3_PL2_FP16_SwishExcitation.aocx |
+| person-vehicle-bike-detection-crossroad-0078 | 2020-3_PL2_FP11_YoloV3_ELU.aocx | 2020-3_PL2_FP16_ELU.aocx |
+| person-vehicle-bike-detection-crossroad-1016 | 2020-3_PL2_FP11_MobileNet_Clamp.aocx | 2020-3_PL2_FP16_SwishExcitation.aocx |
+| product-detection-0001 | 2020-3_PL2_FP11_MobileNet_Clamp.aocx | 2020-3_PL2_FP16_MobileNet_Clamp.aocx |
+| resnet18-xnor-binary-onnx-0001 | 2020-3_PL2_FP11_AlexNet_GoogleNet_Generic.aocx | 2020-3_PL2_FP16_RMNet.aocx |
+| resnet50-binary-0001 | 2020-3_PL2_FP11_AlexNet_GoogleNet_Generic.aocx | 2020-3_PL2_FP16_AlexNet_GoogleNet_SSD300_Generic.aocx |
+| road-segmentation-adas-0001 | 2020-3_PL2_FP11_MobileNet_Clamp.aocx | 2020-3_PL2_FP16_MobileNet_Clamp.aocx |
+| semantic-segmentation-adas-0001 | 2020-3_PL2_FP11_MobileNet_Clamp.aocx | 2020-3_PL2_FP16_AlexNet_GoogleNet_SSD300_Generic.aocx |
+| single-image-super-resolution-1032 | 2020-3_PL2_FP11_MobileNet_Clamp.aocx | 2020-3_PL2_FP16_RMNet.aocx |
+| single-image-super-resolution-1033 | 2020-3_PL2_FP11_MobileNet_Clamp.aocx | 2020-3_PL2_FP16_RMNet.aocx |
+| text-detection-0003 | 2020-3_PL2_FP11_MobileNet_Clamp.aocx | 2020-3_PL2_FP16_MobileNet_Clamp.aocx |
+| text-detection-0004 | 2020-3_PL2_FP11_MobileNet_Clamp.aocx | 2020-3_PL2_FP16_MobileNet_Clamp.aocx |
+| text-image-super-resolution-0001 | 2020-3_PL2_FP11_MobileNet_Clamp.aocx | 2020-3_PL2_FP16_RMNet.aocx |
+| text-recognition-0012 | 2020-3_PL2_FP11_AlexNet_GoogleNet_Generic.aocx | 2020-3_PL2_FP16_AlexNet_GoogleNet_SSD300_Generic.aocx |
+| text-spotting-0002-detector | 2020-3_PL2_FP11_InceptionV1_ResNet_VGG.aocx | 2020-3_PL2_FP16_ResNet_YoloV3.aocx |
+| text-spotting-0002-recognizer-decoder | 2020-3_PL2_FP11_AlexNet_GoogleNet_Generic.aocx | 2020-3_PL2_FP16_AlexNet_GoogleNet_SSD300_Generic.aocx |
+| text-spotting-0002-recognizer-encoder | 2020-3_PL2_FP11_InceptionV1_ResNet_VGG.aocx | 2020-3_PL2_FP16_SqueezeNet_TinyYolo_VGG.aocx |
+| unet-camvid-onnx-0001 | 2020-3_PL2_FP11_InceptionV1_ResNet_VGG.aocx | 2020-3_PL2_FP16_SwishExcitation.aocx |
+| vehicle-attributes-recognition-barrier-0039 | 2020-3_PL2_FP11_SqueezeNet.aocx | 2020-3_PL2_FP16_SqueezeNet_TinyYolo_VGG.aocx |
+| vehicle-detection-adas-0002 | 2020-3_PL2_FP11_YoloV3_ELU.aocx | 2020-3_PL2_FP16_SwishExcitation.aocx |
+| vehicle-detection-adas-binary-0001 | 2020-3_PL2_FP11_AlexNet_GoogleNet_Generic.aocx | 2020-3_PL2_FP16_AlexNet_GoogleNet_SSD300_Generic.aocx |
+| vehicle-license-plate-detection-barrier-0106 | 2020-3_PL2_FP11_MobileNet_Clamp.aocx | 2020-3_PL2_FP16_MobileNet_Clamp.aocx |
+| yolo-v2-ava-0001 | 2020-3_PL2_FP11_SqueezeNet.aocx | 2020-3_PL2_FP16_SqueezeNet_TinyYolo_VGG.aocx |
+| yolo-v2-ava-sparse-35-0001 | 2020-3_PL2_FP11_SqueezeNet.aocx | 2020-3_PL2_FP16_SqueezeNet_TinyYolo_VGG.aocx |
+| yolo-v2-ava-sparse-70-0001 | 2020-3_PL2_FP11_SqueezeNet.aocx | 2020-3_PL2_FP16_SqueezeNet_TinyYolo_VGG.aocx |
+| yolo-v2-tiny-ava-0001 | 2020-3_PL2_FP11_SqueezeNet.aocx | 2020-3_PL2_FP16_ResNet_YoloV3.aocx |
+| yolo-v2-tiny-ava-sparse-30-0001 | 2020-3_PL2_FP11_SqueezeNet.aocx | 2020-3_PL2_FP16_ResNet_YoloV3.aocx |
+| yolo-v2-tiny-ava-sparse-60-0001 | 2020-3_PL2_FP11_SqueezeNet.aocx | 2020-3_PL2_FP16_ResNet_YoloV3.aocx |
+
+</details>
+
+## <a name="TranslatingArchtoBitstream"></a>Translate from Architecture to FPGA Bitstream Files
+
+Various FPGA bitstreams that support CNN are available in the OpenVINO&trade; toolkit package for FPGA.
+
+To select the correct bitstream (`.aocx`) file for an architecture, select a network (for example, Resnet-18) from the table above for either the Intel® Vision Accelerator Design with an Intel® Arria 10 FPGA (Speed Grade 1), Intel® Vision Accelerator Design with an Intel® Arria 10 FPGA (Speed Grade 2) or the Intel&reg; Programmable Acceleration Card (PAC) with Intel&reg; Arria&reg; 10 GX FPGA and note the corresponding architecture.
+
+The following table describes several parameters that might help you to select the proper bitstream for your needs:
+
+| Name                                           | Board                                                                           | Precision | LRN Support | Leaky ReLU Support | PReLU Support | Clamp Support | ELU Support |
+|:------------------------------------------|:--------------------------------------------------------------------------------|:----------|:------------|:-------------------|:--------------|:--------------|:------------|
+| 2020-4_PL2_FP11_AlexNet_GoogleNet_Generic | Intel&reg; Vision Accelerator Design with an Intel&reg; Arria&reg; 10 FPGA (Speed Grade 2) | FP11 | true | true | true | false | false |
+| 2020-4_PL2_FP11_SqueezeNet | Intel&reg; Vision Accelerator Design with an Intel&reg; Arria&reg; 10 FPGA (Speed Grade 2) | FP11 | false | true | true | false | false |
+| 2020-4_PL2_FP11_MobileNet_Clamp | Intel&reg; Vision Accelerator Design with an Intel&reg; Arria&reg; 10 FPGA (Speed Grade 2) | FP11 | false | true | true | true | false |
+| 2020-4_PL2_FP11_InceptionV1_ResNet_VGG | Intel&reg; Vision Accelerator Design with an Intel&reg; Arria&reg; 10 FPGA (Speed Grade 2) | FP11 | false | false | false | false | false |
+| 2020-4_PL2_FP11_RMNet | Intel&reg; Vision Accelerator Design with an Intel&reg; Arria&reg; 10 FPGA (Speed Grade 2) | FP11 | false | true | true | false | true |
+| 2020-4_PL2_FP11_TinyYolo_SSD300 | Intel&reg; Vision Accelerator Design with an Intel&reg; Arria&reg; 10 FPGA (Speed Grade 2) | FP11 | true | true | true | false | false |
+| 2020-4_PL2_FP11_YoloV3_ELU | Intel&reg; Vision Accelerator Design with an Intel&reg; Arria&reg; 10 FPGA (Speed Grade 2) | FP11 | false | true | true | false | true |
+| 2020-4_PL2_FP11_Streaming_InternalUseOnly | Intel&reg; Vision Accelerator Design with an Intel&reg; Arria&reg; 10 FPGA (Speed Grade 2) | FP11 | false | false | false | false | false |
+| 2020-4_PL2_FP11_Streaming_Slicing_InternalUseOnly | Intel&reg; Vision Accelerator Design with an Intel&reg; Arria&reg; 10 FPGA (Speed Grade 2) | FP11 | false | false | false | false | false |
+| 2020-4_PL2_FP11_SwishExcitation | Intel&reg; Vision Accelerator Design with an Intel&reg; Arria&reg; 10 FPGA (Speed Grade 2) | FP11 | false | false | false | false | false |
+| 2020-4_PL2_FP16_AlexNet_GoogleNet_SSD300_Generic | Intel&reg; Vision Accelerator Design with an Intel&reg; Arria&reg; 10 FPGA (Speed Grade 2) | FP16 | true | true | true | false | false |
+| 2020-4_PL2_FP16_ELU | Intel&reg; Vision Accelerator Design with an Intel&reg; Arria&reg; 10 FPGA (Speed Grade 2) | FP16 | false | true | true | false | true |
+| 2020-4_PL2_FP16_MobileNet_Clamp | Intel&reg; Vision Accelerator Design with an Intel&reg; Arria&reg; 10 FPGA (Speed Grade 2) | FP16 | false | true | true | true | false |
+| 2020-4_PL2_FP16_ResNet_YoloV3 | Intel&reg; Vision Accelerator Design with an Intel&reg; Arria&reg; 10 FPGA (Speed Grade 2) | FP16 | false | true | true | false | false |
+| 2020-4_PL2_FP16_RMNet | Intel&reg; Vision Accelerator Design with an Intel&reg; Arria&reg; 10 FPGA (Speed Grade 2) | FP16 | false | true | true | false | true |
+| 2020-4_PL2_FP16_SqueezeNet_TinyYolo_VGG | Intel&reg; Vision Accelerator Design with an Intel&reg; Arria&reg; 10 FPGA (Speed Grade 2) | FP16 | false | true | true | false | false |
+| 2020-4_PL2_FP16_SqueezeNet_TinyYolo_VGG | Intel&reg; Vision Accelerator Design with an Intel&reg; Arria&reg; 10 FPGA (Speed Grade 2) | FP16 | false | false | false | false | false |
+| 2020-4_RC_FP11_AlexNet_GoogleNet_Generic | Intel&reg; Programmable Acceleration Card with Intel&reg; Arria&reg; 10 GX FPGA | FP11 | true | true | true | false | false |
+| 2020-4_RC_FP11_RMNet | Intel&reg; Programmable Acceleration Card with Intel&reg; Arria&reg; 10 GX FPGA | FP11 | false | true | true | false | true |
+| 2020-4_RC_FP11_Streaming_InternalUseOnly | Intel&reg; Programmable Acceleration Card with Intel&reg; Arria&reg; 10 GX FPGA | FP11 | true | false | false | false | false |
+| 2020-4_RC_FP11_Streaming_Slicing_InternalUseOnly | Intel&reg; Programmable Acceleration Card with Intel&reg; Arria&reg; 10 GX FPGA | FP11 | true | false | false | false | false |
+| 2020-4_RC_FP11_ELU | Intel&reg; Programmable Acceleration Card with Intel&reg; Arria&reg; 10 GX FPGA | FP11 | false | true | true | false | true |
+| 2020-4_RC_FP11_SwishExcitation | Intel&reg; Programmable Acceleration Card with Intel&reg; Arria&reg; 10 GX FPGA | FP11 | false | false | false | false | false |
+| 2020-4_RC_FP11_InceptionV1_ResNet_SqueezeNet_TinyYolo_YoloV3 | Intel&reg; Programmable Acceleration Card with Intel&reg; Arria&reg; 10 GX FPGA | FP11 | false | true | true | false | false |
+| 2020-4_RC_FP11_MobileNet_Clamp | Intel&reg; Programmable Acceleration Card with Intel&reg; Arria&reg; 10 GX FPGA | FP11 | false | true | true | true | false |
+| 2020-4_RC_FP16_AlexNet_GoogleNet_Generic | Intel&reg; Programmable Acceleration Card with Intel&reg; Arria&reg; 10 GX FPGA | FP16 | true | true | true | false | false |
+| 2020-4_RC_FP16_InceptionV1_SqueezeNet_TinyYolo_VGG | Intel&reg; Programmable Acceleration Card with Intel&reg; Arria&reg; 10 GX FPGA | FP16 | false | true | true | false | false |
+| 2020-4_RC_FP16_RMNet | Intel&reg; Programmable Acceleration Card with Intel&reg; Arria&reg; 10 GX FPGA | FP16 | false | true | true | false | true |
+| 2020-4_RC_FP16_SwishExcitation | Intel&reg; Programmable Acceleration Card with Intel&reg; Arria&reg; 10 GX FPGA | FP16 | false | false | false | false | false |
+| 2020-4_RC_FP16_MobileNet_Clamp | Intel&reg; Programmable Acceleration Card with Intel&reg; Arria&reg; 10 GX FPGA | FP16 | false | true | true | true | false |
+| 2020-4_RC_FP16_ResNet_YoloV3 | Intel&reg; Programmable Acceleration Card with Intel&reg; Arria&reg; 10 GX FPGA | FP16 | false | true | true | false | false |
+| 2020-4_RC_FP16_InceptionV1_SqueezeNet_YoloV3 | Intel&reg; Programmable Acceleration Card with Intel&reg; Arria&reg; 10 GX FPGA | FP16 | false | true | true | false | false |
+
+## Set Environment for Running the FPGA Plugin
+
+To make the FPGA plugin run directly or through the heterogeneous plugin, set up the environment:
+1. Set up environment to access Intel&reg; FPGA RTE for OpenCL:
+```
+source /opt/altera/aocl-pro-rte/aclrte-linux64/init_opencl.sh
+```
+2. Set the following environment variable and program the board with a DLA bitstream. Programming of the board is not supported during runtime and must be done before running an application.
+
+  | Variable                           | Setting                                                                                                                                                                                                                                                                                                                                                                                                                                       |
+  | :----------------------------------| :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | ACL_PCIE_USE_JTAG_PROGRAMMING      | Set this variable to a value of 1 to force FPGA reprogramming using JTAG                                                                                                                                                                                                                                                                                                                                                                      |
+
+## Analyzing Heterogeneous Execution
+
+Besides generation of .dot files, you can use the error listening mechanism:
+
+```cpp
+class FPGA_ErrorListener : public InferenceEngine::IErrorListener
+{
+public:
+    virtual void onError(const char *msg) noexcept override {
+        std::cout << msg;
+    }
+};
+...
+FPGA_ErrorListener err_listener;
+core.SetLogCallback(err_listener); // will be used for FPGA device as well
+```
+If during network loading some layers are decided to be executed on a fallback plugin, the following message is printed:
+
+```cpp
+Layer (Name: detection_out, Type: DetectionOutput) is not supported:
+	custom or unknown.
+	Has (3) sets of inputs, must be 1, or 2.
+	Input dimensions (2) should be 4.
+```
+
+## Multiple FPGA Devices Support
+
+The Inference Engine FPGA plugin provides an ability to load different networks on multiple FPGA devices. For example, to load two networks AlexNet and MobileNet v2 on two different FPGA devices, follow the steps below:
+
+1. Program each FGPA device with a corresponding bitstream:
+```bash
+aocl program acl0 2019R3_PV_PL1_FP16_AlexNet_GoogleNet_InceptionV1_SSD300_Generic.aocx
+```
+```bash
+aocl program acl1 2019R3_PV_PL1_FP16_MobileNet_Clamp.aocx
+```
+For more information about bitstream programming instructions, refer to [Installation Guide for Linux* with Support for FPGA](Supported_Devices.md)
+2. All FPGA devices are enumerated with unique ID starting from `0`. By default, all networks are loaded to the default
+device with ID `0`. If you want to load a network on a particular non-default device, specify the `KEY_DEVICE_ID`
+parameter for C++ and `DEVICE_ID` parameter for Python\*.
+The following code snippets demonstrates how to load the AlexNet network on the FPGA device with ID `0` and the
+MobileNet v2 network on the device with ID `1`:
+    * With C++:
+```cpp
+InferenceEngine::Core core;
+
+// Load AlexNet network on the first FPGA device programmed with bitstream supporting AlexNet
+auto alexnetNetwork = core.ReadNetwork("alexnet.xml");
+auto exeNetwork1 = core.LoadNetwork(alexnetNetwork, "FPGA.0");
+
+// Load MobileNet network on the second FPGA device programmed with MobileNet bitstream
+auto mobilenetNetwork = core.ReadNetwork("mobilenet_v2.xml");
+auto exeNetwork2 = core.LoadNetwork(mobilenetNetwork, "FPGA", { { KEY_DEVICE_ID, "1" } });
+```
+    * With Python:
+```python
+# Load AlexNet network on the first FPGA device programmed with bitstream supporting AlexNet
+net1 = IENetwork(model="alexnet.xml", weights="alexnet.bin")
+plugin.load(network=net1, config={"DEVICE_ID": "0"})
+
+# Load MobileNet network on the second FPGA device programmed with MobileNet bitstream
+net2 = IENetwork(model="mobilenet_v2.xml", weights="mobilenet_v2.bin")
+plugin.load(network=net2, config={"DEVICE_ID": "1"})
+```
+Note that you have to use asynchronous infer requests to utilize several FPGA devices, otherwise the execution on devices is performed sequentially.
+
+## Import and Export Network Flow
+
+Since the 2019 R4 release, FPGA and HETERO plugins support the export and import flow, which allows to export a compiled network from a plugin to a binary blob by running the command below:
+
+```bash
+$ ./compile_tool -m resnet.xml -DLA_ARCH_NAME 4x2x16x32_fp16_sb9408_fcd1024_actk4_poolk4_normk1_owk2_image300x300x8192_mbfr -d HETERO:FPGA,CPU
+Inference Engine: 
+	API version ............ 2.1
+	Build .................. 6db44e09a795cb277a63275ea1395bfcb88e46ac
+	Description ....... API
+Done
+```
+
+Once the command is executed, the binary blob named `resnet.blob` is created at the working directory. Refer to the [Compile tool](../../../inference-engine/tools/compile_tool/README.md) documentation for more details.
+
+A compiled binary blob can be later imported via `InferenceEngine::Core::Import`:
+
+```cpp
+InferenceEngine::Core core;
+std::ifstream strm("resnet.blob");
+auto execNetwork = core.Import(strm);
+```
+
+## How to Interpret Performance Counters
+
+As a result of collecting performance counters using <code>InferenceEngine::InferRequest::GetPerformanceCounts</code> you can find out performance data about execution on FPGA, pre-processing and post-processing data and data transferring from/to FPGA card.
+
+If network is sliced to two parts that are executed on CPU, you can find performance data about Intel&reg; MKL-DNN kernels, their types, and other useful information.
+
+## Limitations of the FPGA Support for CNN
+
+The Inference Engine FPGA plugin has limitations on network topologies, kernel parameters, and batch size.
+
+* Depending on the bitstream loaded on the target device, the FPGA performs calculations with precision rates ranging from FP11 to FP16. This might have accuracy implications. Use the [Accuracy Checker](@ref omz_tools_accuracy_checker_README) to verify the network accuracy on the validation data set.
+* Networks that have many CNN layers that are not supported on FPGA stayed in topologies between supported layers might lead to dividing of graph to many subgraphs that might lead to `CL_OUT_OF_HOST_MEMORY` error. These topologies are not FPGA friendly for this release.
+* When you use the heterogeneous plugin, the affinity and distribution of nodes by devices depends on the FPGA bitstream that you use. Some layers might not be supported by a bitstream or parameters of the layer are not supported by the bitstream.
+
+## See Also
+* [Supported Devices](Supported_Devices.md)

docs/IE_DG/supported_plugins/GNA.md

@@ -2,98 +2,95 @@
 
 ## Introducing the GNA Plugin
 
-Intel® Gaussian & Neural Accelerator is a low-power neural coprocessor for continuous inference at the edge.
+Intel® Gaussian & Neural Accelerator is a low-power neural coprocessor for continuous inference at the edge.
 
-Intel® GNA is not intended to replace classic inference devices such as
-CPU, graphics processing unit (GPU), or vision processing unit (VPU). It is designed for offloading 
+Intel® GNA is not intended to replace classic inference devices such as
+CPU, graphics processing unit (GPU), or vision processing unit (VPU) . It is designed for offloading 
 continuous inference workloads including but not limited to noise reduction or speech recognition 
 to save power and free CPU resources.
 
-The GNA plugin provides a way to run inference on Intel® GNA, as well as in the software execution mode on CPU.
+The GNA plugin provides a way to run inference on Intel® GNA, as well as in the software execution mode on CPU.
 
-## Devices with Intel® GNA
+## Devices with Intel® GNA
 
-Devices with Intel® GNA support:
+Devices with Intel® GNA support:
 
-* [Intel® Speech Enabling Developer Kit](https://www.intel.com/content/www/us/en/support/articles/000026156/boards-and-kits/smart-home.html)
+* [Intel® Speech Enabling Developer Kit](https://www.intel.com/content/www/us/en/support/articles/000026156/boards-and-kits/smart-home.html)
 
-* [Amazon Alexa\* Premium Far-Field Developer Kit](https://developer.amazon.com/en-US/alexa/alexa-voice-service/dev-kits/amazon-premium-voice)
+* [Amazon Alexa* Premium Far-Field Developer Kit](https://developer.amazon.com/en-US/alexa/alexa-voice-service/dev-kits/amazon-premium-voice)
 
-* [Intel® Pentium® Silver Processors N5xxx, J5xxx and Intel® Celeron® Processors N4xxx, J4xxx](https://ark.intel.com/content/www/us/en/ark/products/codename/83915/gemini-lake.html):
-	- Intel® Pentium® Silver J5005 Processor
-	- Intel® Pentium® Silver N5000 Processor
-	- Intel® Celeron® J4005 Processor
-	- Intel® Celeron® J4105 Processor
-	- Intel® Celeron® Processor N4100
-	- Intel® Celeron® Processor N4000
+* [Gemini Lake](https://ark.intel.com/content/www/us/en/ark/products/codename/83915/gemini-lake.html):
+	- Intel® Pentium® Silver J5005 Processor
+	- Intel® Pentium® Silver N5000 Processor
+	- Intel® Celeron® J4005 Processor
+	- Intel® Celeron® J4105 Processor
+	- Intel® Celeron® Processor N4100
+	- Intel® Celeron® Processor N4000
 
-* [Intel® Core™ Processors (formerly codenamed Cannon Lake)](https://ark.intel.com/content/www/us/en/ark/products/136863/intel-core-i3-8121u-processor-4m-cache-up-to-3-20-ghz.html):
-Intel® Core™ i3-8121U Processor
+* [Cannon Lake](https://ark.intel.com/content/www/us/en/ark/products/136863/intel-core-i3-8121u-processor-4m-cache-up-to-3-20-ghz.html):
+Intel® Core™ i3-8121U Processor
 
-* [10th Generation Intel® Core™ Processors (formerly codenamed Ice Lake)](https://ark.intel.com/content/www/us/en/ark/products/codename/74979/ice-lake.html):
-	- Intel® Core™ i7-1065G7 Processor
-	- Intel® Core™ i7-1060G7 Processor
-	- Intel® Core™ i5-1035G4 Processor
-	- Intel® Core™ i5-1035G7 Processor
-	- Intel® Core™ i5-1035G1 Processor
-	- Intel® Core™ i5-1030G7 Processor
-	- Intel® Core™ i5-1030G4 Processor
-	- Intel® Core™ i3-1005G1 Processor
-	- Intel® Core™ i3-1000G1 Processor
-	- Intel® Core™ i3-1000G4 Processor
+* [Ice Lake](https://ark.intel.com/content/www/us/en/ark/products/codename/74979/ice-lake.html):
+	- Intel® Core™ i7-1065G7 Processor
+	- Intel® Core™ i7-1060G7 Processor
+	- Intel® Core™ i5-1035G4 Processor
+	- Intel® Core™ i5-1035G7 Processor
+	- Intel® Core™ i5-1035G1 Processor
+	- Intel® Core™ i5-1030G7 Processor
+	- Intel® Core™ i5-1030G4 Processor
+	- Intel® Core™ i3-1005G1 Processor
+	- Intel® Core™ i3-1000G1 Processor
+	- Intel® Core™ i3-1000G4 Processor
 
-* All [11th Generation Intel® Core™ Processors (formerly codenamed Tiger Lake)](https://ark.intel.com/content/www/us/en/ark/products/codename/88759/tiger-lake.html).
-
-> **NOTE**: On platforms where Intel® GNA is not enabled in the BIOS, the driver cannot be installed, so the GNA plugin uses the software emulation mode only.
+> **NOTE**: On platforms where Intel® GNA is not enabled in the BIOS, the driver cannot be installed, so the GNA plugin uses the software emulation mode only.
 
 ## Drivers and Dependencies
 
-Intel® GNA hardware requires a driver to be installed on the system.
+Intel® GNA hardware requires a driver to be installed on the system.
 
 * Linux\* OS:
-[Download Intel® GNA driver for Ubuntu Linux 18.04.3 LTS (with HWE Kernel version 5.0+)](https://download.01.org/opencv/drivers/gna/)
+[Download Intel® GNA driver for Ubuntu Linux 18.04.3 LTS (with HWE Kernel version 5.0+)](https://download.01.org/opencv/drivers/gna/)
 
 * Windows\* OS:
-Intel® GNA driver for Windows is available through Windows Update\*
+Intel® GNA driver for Windows is available through Windows Update\*
 
 ## Models and Layers Limitations
 
-Because of specifics of hardware architecture, Intel® GNA supports a limited set of layers, their kinds and combinations.
-For example, you should not expect the GNA Plugin to be able to run computer vision models, except those specifically adapted 
-for the GNA Plugin, because the plugin does not fully support 2D convolutions.
-
-For the list of supported layers, see the **GNA** column of the **Supported Layers** section in [Supported Devices](Supported_Devices.md).
+Because of specifics of hardware architecture, Intel® GNA supports a limited set of layers, their kinds and combinations.
+For example, you should not expect the GNA Plugin to be able to run computer vision models, except those specifically adapted for the GNA Plugin, because the plugin does not fully support
+2D convolutions.
 
+The list of supported layers can be found
+[here](Supported_Devices.md) (see the GNA column of Supported Layers section).
 Limitations include:
 
-- Only 1D convolutions are natively supported in the models converted from:
-	- [Kaldi](../../MO_DG/prepare_model/convert_model/Convert_Model_From_Kaldi.md) framework
-	- [TensorFlow](../../MO_DG/prepare_model/convert_model/Convert_Model_From_TensorFlow.md) framework. For TensorFlow models, use the `--disable_nhwc_to_nchw` option when running the Model Optimizer.
-- The number of output channels for convolutions must be a multiple of 4.
-- Permute layer support is limited to the cases where no data reordering is needed or when reordering is happening for two dimensions, at least one of which is not greater than 8.
+- Only 1D convolutions (in the models converted from [Kaldi](../../MO_DG/prepare_model/convert_model/Convert_Model_From_Kaldi.md) framework) are natively supported
+- The number of output channels for convolutions must be a multiple of 4
+- Permute layer support is limited to the cases where no data reordering is needed, or when reordering is happening for 2 dimensions, at least one of which is not greater than 8
+- Power layer only supports the power parameter equal to 1
 
 #### Experimental Support for 2D Convolutions
 
-The Intel® GNA hardware natively supports only 1D convolution.
+The Intel® GNA hardware natively supports only 1D convolution.
 
-However, 2D convolutions can be mapped to 1D when a convolution kernel moves in a single direction. GNA Plugin performs such a transformation for Kaldi `nnet1` convolution. From this perspective, the Intel® GNA hardware convolution operation accepts an `NHWC` input and produces an `NHWC` output. Because OpenVINO™ only supports the `NCHW` layout, you may need to insert `Permute` layers before or after convolutions.
+However, 2D convolutions can be mapped to 1D when a convolution kernel moves in a single direction. Such a transformation is performed by the GNA Plugin for Kaldi `nnet1` convolution. From this perspective, the Intel® GNA hardware convolution operation accepts a `NHWC` input and produces `NHWC` output. Because OpenVINO™ only supports the `NCHW` layout, it may be necessary to insert `Permute` layers before or after convolutions.
 
-For example, the Kaldi model optimizer inserts such a permute after convolution for the [rm_cnn4a network](https://download.01.org/openvinotoolkit/models_contrib/speech/kaldi/rm_cnn4a_smbr/). This `Permute` layer is automatically removed by the GNA Plugin, because the Intel® GNA hardware convolution layer already produces the required `NHWC` result.
+For example, the Kaldi model optimizer inserts such a permute after convolution for the [rm_cnn4a network](https://download.01.org/openvinotoolkit/models_contrib/speech/kaldi/rm_cnn4a_smbr/). This `Permute` layer is automatically removed by the GNA Plugin, because the Intel® GNA hardware convolution layer already produces the required `NHWC` result.
 
 ## Operation Precision
 
-Intel® GNA essentially operates in the low-precision mode, which represents a mix of 8-bit (`I8`), 16-bit (`I16`), and 32-bit (`I32`) integer computations. Outputs calculated using a reduced integer precision are different from the scores calculated using the floating point format, for example, `FP32` outputs calculated on CPU using the Inference Engine [CPU Plugin](CPU.md).
+Intel® GNA essentially operates in the low-precision mode, which represents a mix of 8-bit (`I8`), 16-bit (`I16`), and 32-bit (`I32`) integer computations, so compared to 32-bit floating point (`FP32`) results – for example, calculated on CPU using Inference Engine [CPU Plugin](CPU.md) – outputs calculated using reduced integer precision are different from the scores calculated using floating point.
 
-Unlike other plugins supporting low-precision execution, the GNA plugin calculates quantization factors at the model loading time, so you can run a model without calibration.
+Unlike other plugins supporting low-precision execution, the GNA plugin calculates quantization factors at the model loading time, so a model can run without calibration.
 
-## <a name="execution-modes">Execution Modes</a>
+## <a name="execution-models">Execution Modes</a>
 
 | Mode | Description |
 | :---------------------------------| :---------------------------------------------------------|
-| `GNA_AUTO` | Uses Intel® GNA if available, otherwise uses software execution mode on CPU. |
-| `GNA_HW` | Uses Intel® GNA if available, otherwise raises an error. |
-| `GNA_SW` | *Deprecated*. Executes the GNA-compiled graph on CPU performing calculations in the same precision as the Intel® GNA, but not in the bit-exact mode. |
-| `GNA_SW_EXACT` | Executes the GNA-compiled graph on CPU performing calculations in the same precision as the Intel® GNA in the bit-exact mode. |
+| `GNA_AUTO` | Uses Intel&reg; GNA if available, otherwise uses software execution mode on CPU. |
+| `GNA_HW` | Uses Intel&reg; GNA if available, otherwise raises an error. |
+| `GNA_SW` | *Deprecated*. Executes the GNA-compiled graph on CPU performing calculations in the same precision as the Intel&reg; GNA, but not in the bit-exact mode. |
+| `GNA_SW_EXACT` | Executes the GNA-compiled graph on CPU performing calculations in the same precision as the Intel&reg; GNA in the bit-exact mode. |
 | `GNA_SW_FP32` | Executes the GNA-compiled graph on CPU but substitutes parameters and calculations from low precision to floating point (`FP32`). |
 
 ## Supported Configuration Parameters
@@ -101,42 +98,42 @@ Unlike other plugins supporting low-precision execution, the GNA plugin calculat
 The plugin supports the configuration parameters listed below.
 The parameters are passed as `std::map<std::string, std::string>` on `InferenceEngine::Core::LoadNetwork` or `InferenceEngine::SetConfig`.
 
-You can change the `KEY_GNA_DEVICE_MODE` parameter at run time using `InferenceEngine::ExecutableNetwork::SetConfig`, which works for any value excluding `GNA_SW_FP32`. This enables you to switch the
+The parameter `KEY_GNA_DEVICE_MODE` can also be changed at run time using `InferenceEngine::ExecutableNetwork::SetConfig` (for any values excluding `GNA_SW_FP32`). This allows switching the
 execution between software emulation mode and hardware emulation mode after the model is loaded.
 
 The parameter names below correspond to their usage through API keys, such as `GNAConfigParams::KEY_GNA_DEVICE_MODE` or `PluginConfigParams::KEY_PERF_COUNT`.
-When specifying key values as raw strings, that is, when using Python API, omit the `KEY_` prefix.
+When specifying key values as raw strings (that is, when using Python API), omit the `KEY_` prefix.
 
 | Parameter Name                    | Parameter Values                                          | Default Value     | Description                                                              |
 | :---------------------------------| :---------------------------------------------------------| :-----------| :------------------------------------------------------------------------|
-| `KEY_GNA_COMPACT_MODE`            | `YES`/`NO`                                                | `YES`       | Enables I/O buffers reuse to save space. Makes debugging harder.                 |
-| `KEY_GNA_SCALE_FACTOR`            | `FP32` number                                             | 1.0         | Sets the scale factor to use for input quantization.                               |
-| `KEY_GNA_DEVICE_MODE`             | `GNA_AUTO`/`GNA_HW`/`GNA_SW_EXACT`/`GNA_SW_FP32` | `GNA_AUTO`  | One of the modes described in <a href="#execution-modes">Execution Modes</a> |
-| `KEY_GNA_FIRMWARE_MODEL_IMAGE`    | `std::string`                                             | `""`        | Sets the name for the embedded model binary dump file.                                 |
-| `KEY_GNA_PRECISION`               | `I16`/`I8`                                                | `I16`       | Sets the preferred integer weight resolution for quantization. |
-| `KEY_PERF_COUNT`                  | `YES`/`NO`                                                | `NO`        | Turns on performance counters reporting.                                   |
-| `KEY_GNA_LIB_N_THREADS`           | 1-127 integer number                                      | 1           | Sets the number of GNA accelerator library worker threads used for inference computation in software modes.
+| `KEY_GNA_COMPACT_MODE`            | `YES`/`NO`                                                | `YES`       | Reuse I/O buffers to save space (makes debugging harder)                 |
+| `KEY_GNA_SCALE_FACTOR`            | `FP32` number                                             | 1.0         | Scale factor to use for input quantization                               |
+| `KEY_GNA_DEVICE_MODE`             | `GNA_AUTO`/`GNA_HW`/`GNA_SW_EXACT`/`GNA_SW_FP32` | `GNA_AUTO`  | One of the modes described <a name="execution-models">Execution Models</a> |
+| `KEY_GNA_FIRMWARE_MODEL_IMAGE`    | `std::string`                                             | `""`        | Name for embedded model binary dump file                                 |
+| `KEY_GNA_PRECISION`               | `I16`/`I8`                                                | `I16`       | Hint to GNA plugin: preferred integer weight resolution for quantization |
+| `KEY_PERF_COUNT`                  | `YES`/`NO`                                                | `NO`        | Turn on performance counters reporting                                   |
+| `KEY_GNA_LIB_N_THREADS`           | 1-127 integer number                                      | 1           | Sets the number of GNA accelerator library worker threads used for inference computation in software modes
 
 ## How to Interpret Performance Counters
 
 As a result of collecting performance counters using `InferenceEngine::InferRequest::GetPerformanceCounts`, you can find various performance data about execution on GNA.
-Returned map stores a counter description as a key, and a counter value in the `realTime_uSec` field of the `InferenceEngineProfileInfo` structure. Current GNA implementation calculates counters for the whole utterance scoring and does not provide per-layer information. The API enables you to retrieve counter units in cycles, you can convert cycles to seconds as follows:
+Returned map stores a counter description as a key, counter value is stored in the `realTime_uSec` field of the `InferenceEngineProfileInfo` structure. Current GNA implementation calculates counters for the whole utterance scoring and does not provide per-layer information. API allows to retrieve counter units in cycles, but they can be converted to seconds as follows:
 
 ```
 seconds = cycles / frequency
 ```
 
-Refer to the table below to learn about the frequency of Intel® GNA inside a particular processor.
-Processor | Frequency of Intel® GNA
+Refer to the table below to learn about the frequency of Intel&reg; GNA inside a particular processor.
+Processor | Frequency of Intel&reg; GNA
 ---|---
-Intel® Ice Lake processors| 400MHz
-Intel® Core™ i3-8121U processor| 400MHz
-Intel® Gemini Lake  processors | 200MHz
+Intel&reg; Ice Lake processors| 400MHz
+Intel&reg; Core&trade; i3-8121U processor| 400MHz
+Intel&reg; Gemini Lake  processors | 200MHz
 
 Performance counters provided for the time being:
 
 * Scoring request performance results
-	* Number of total cycles spent on scoring in hardware including compute and memory stall cycles
+	* Number of total cycles spent on scoring in hardware (including compute and memory stall cycles)
 	* Number of stall cycles spent in hardware
 
 ## Multithreading Support in GNA Plugin
@@ -151,40 +148,16 @@ The GNA plugin supports the following configuration parameters for multithreadin
 
 ## Network Batch Size
 
-Intel® GNA plugin supports the processing of context-windowed speech frames in batches of 1-8 frames in one
+Intel&reg; GNA plugin supports the processing of context-windowed speech frames in batches of 1-8 frames in one
 input blob using `InferenceEngine::ICNNNetwork::setBatchSize`. Increasing batch size only improves efficiency of `Fully Connected` layers.
 
 > **NOTE**: For networks with `Convolutional`, `LSTM`, or `Memory` layers, the only supported batch size is 1.
 
 ## Compatibility with Heterogeneous Plugin
 
-Heterogeneous plugin was tested with the Intel® GNA as a primary device and CPU as a secondary device. To run inference of networks with layers unsupported by the GNA plugin, such as Softmax, use the Heterogeneous plugin with the `HETERO:GNA,CPU` configuration.
+Heterogeneous plugin was tested with the Intel&reg; GNA as a primary device and CPU as a secondary device. To run inference of networks with layers unsupported by the GNA plugin (for example, Softmax), use the Heterogeneous plugin with the `HETERO:GNA,CPU` configuration. For the list of supported networks, see the [Supported Frameworks](#supported-frameworks).
 
-> **NOTE:** Due to limitation of the Intel® GNA backend library, heterogenous support is limited to cases where in the resulted sliced graph, only one subgraph is scheduled to run on GNA\_HW or GNA\_SW devices.
-
-## Recovery from Interruption by High-Priority Windows Audio Processes\*
-
-GNA is designed for real-time workloads such as noise reduction.
-For such workloads, processing should be time constrained, otherwise extra delays may cause undesired effects such as
-*audio glitches*. To make sure that processing can satisfy real-time requirements, the GNA driver provides a Quality of Service
-(QoS) mechanism, which interrupts requests that might cause high-priority Windows audio processes to miss
-the schedule, thereby causing long running GNA tasks to terminate early.
-
-Applications should be prepared for this situation.
-If an inference in the `GNA_HW` mode cannot be executed because of such an interruption, then `InferRequest::Wait()` returns status code
-`StatusCode::INFER_NOT_STARTED`. In future releases, it will be changed to a more meaningful status code.
-
-Any application working with GNA must properly react to this code.
-One of the strategies to adapt an application:
-
-1. Immediately switch to the GNA_SW emulation mode:
-```cpp
-std::map<std::string, Parameter> newConfig;
-newConfig[GNAConfigParams::KEY_GNA_DEVICE_MODE] = Parameter("GNA_SW_EXACT");
-executableNet.SetConfig(newConfig);
-
-```
-2. Resubmit and switch back to GNA_HW expecting that the competing application has finished.
+> **NOTE:** Due to limitation of the Intel&reg; GNA backend library, heterogenous support is limited to cases where in the resulted sliced graph, only one subgraph is scheduled to run on GNA\_HW or GNA\_SW devices.
 
 ## See Also
 

docs/IE_DG/supported_plugins/HDDL.md

@@ -2,15 +2,15 @@
 
 ## Introducing HDDL Plugin
 
-The Inference Engine HDDL plugin is developed for inference of neural networks on the Intel® Vision Accelerator Design with Intel® Movidius™ VPUs. It is designed for use cases which require large throughputs of deep learning inference. It provides dozens of times the throughput as the MYRIAD Plugin does.
+The Inference Engine HDDL plugin is developed for inference of neural networks on Intel® Vision Accelerator Design with Intel® Movidius™ VPUs which is designed for use cases those require large throughput of deep learning inference. It provides dozens amount of throughput as MYRIAD Plugin.
 
 ## Installation on Linux* OS
 
-For installation instructions, refer to the [Installation Guide for Linux*](VPU.md).
+For installation instructions, refer to the [Installation Guide for Linux\*](VPU.md).
 
 ## Installation on Windows* OS
 
-For installation instructions, refer to the [Installation Guide for Windows*](Supported_Devices.md).
+For installation instructions, refer to the [Installation Guide for Windows\*](Supported_Devices.md).
 
 ## Supported networks
 
@@ -30,7 +30,7 @@ In addition to common parameters for Myriad plugin and HDDL plugin, HDDL plugin
 | KEY_VPU_HDDL_STREAM_ID                | string           | empty string | Allows to execute inference on a specified device.                              |
 | KEY_VPU_HDDL_DEVICE_TAG               | string           | empty string | Allows to allocate/deallocate networks on specified devices.                    |
 | KEY_VPU_HDDL_BIND_DEVICE              | YES/NO           | NO           | Whether the network should bind to a device. Refer to vpu_plugin_config.hpp.    |
-| KEY_VPU_HDDL_RUNTIME_PRIORITY         | singed int       | 0            | Specify the runtime priority of a device among all devices that are running the same network. Refer to vpu_plugin_config.hpp. |
+| KEY_VPU_HDDL_RUNTIME_PRIORITY         | singed int       | 0            | Specify the runtime priority of a device among all devices that running a same network Refer to vpu_plugin_config.hpp. |
 
 ## See Also
 

docs/IE_DG/supported_plugins/HETERO.md

@@ -9,13 +9,12 @@ Purposes to execute networks in heterogeneous mode
 * To utilize all available hardware more efficiently during one inference
 
 The execution through heterogeneous plugin can be divided to two independent steps:
-* Setting of affinity to layers
+* Setting of affinity to layers (binding them to devices in <code>InferenceEngine::ICNNNetwork</code>)
 * Loading a network to the Heterogeneous plugin, splitting the network to parts, and executing them through the plugin
 
 These steps are decoupled. The setting of affinity can be done automatically using fallback policy or in manual mode.
 
 The fallback automatic policy means greedy behavior and assigns all layers which can be executed on certain device on that device follow priorities.
-Automatic policy does not take into account such plugin peculiarities as inability to infer some layers without other special layers placed before of after that layers. It is plugin responsibility to solve such cases. If device plugin does not support subgraph topology constructed by Hetero plugin affinity should be set manually.
 
 Some of the topologies are not friendly to heterogeneous execution on some devices or cannot be executed in such mode at all.
 Example of such networks might be networks having activation layers which are not supported on primary device.
@@ -26,12 +25,7 @@ In this case you can define heaviest part manually and set affinity thus way to
 ## Annotation of Layers per Device and Default Fallback Policy
 Default fallback policy decides which layer goes to which device automatically according to the support in dedicated plugins (FPGA, GPU, CPU, MYRIAD).
 
-Another way to annotate a network is to set affinity manually using <code>ngraph::Node::get_rt_info</code> with key `"affinity"`:
-
-```cpp
-for (auto && op : function->get_ops())
-    op->get_rt_info()["affinity"] = std::shared_ptr<ngraph::VariantWrapper<std::string>>("CPU");
-```
+Another way to annotate a network is setting affinity manually using <code>CNNLayer::affinity</code> field. This field accepts string values of devices like "CPU" or "FPGA".
 
 The fallback policy does not work if even one layer has an initialized affinity. The sequence should be calling of automating affinity settings and then fix manually.
 ```cpp
@@ -49,10 +43,8 @@ InferenceEngine::QueryNetworkResult res = core.QueryNetwork(network, device, { }
 res.supportedLayersMap["layerName"] = "CPU";
 
 // set affinities to network
-for (auto&& node : function->get_ops()) {
-    auto& affinity = res.supportedLayersMap[node->get_friendly_name()];
-    // Store affinity mapping using node runtime information
-    node->get_rt_info()["affinity"] = std::make_shared<ngraph::VariantWrapper<std::string>>(affinity);
+for (auto && layer : res.supportedLayersMap) {
+	network.getLayerByName(layer->first)->affinity = layer->second;
 }
 
 // load network with affinities set before
@@ -66,8 +58,6 @@ auto network = core.ReadNetwork("Model.xml");
 auto executable_network = core.LoadNetwork(network, "HETERO:FPGA,CPU");
 ```
 
-> **NOTE**: `InferenceEngine::Core::QueryNetwork` does not depend on affinities set by a user, but queries for layer support based on device capabilities.
-
 
 ## Details of Splitting Network and Execution
 During loading of the network to heterogeneous plugin, network is divided to separate parts and loaded to dedicated plugins.
@@ -80,7 +70,9 @@ Precision for inference in heterogeneous plugin is defined by
 
 Examples:
 * If you want to execute GPU with CPU fallback with FP16 on GPU, you need to use only FP16 IR.
-* If you want to execute on FPGA with CPU fallback, you can use any precision for IR. The execution on FPGA is defined by bitstream, the execution on CPU happens in FP32.
+Weight are converted from FP16 to FP32 automatically for execution on CPU by heterogeneous plugin automatically.
+* If you want to execute on FPGA with CPU fallback, you can use any precision for IR. The execution on FPGA is defined by bitstream,
+the execution on CPU happens in FP32.
 
 Samples can be used with the following command:
 

docs/IE_DG/supported_plugins/MYRIAD.md

@@ -6,12 +6,11 @@ The Inference Engine MYRIAD plugin is developed for inference of neural networks
 
 ## Installation on Linux* OS
 
-For installation instructions, refer to the [Installation Guide for Linux*](../../install_guides/installing-openvino-linux.md).
-
+For installation instructions, refer to the [Installation Guide for Linux*](../../../inference-engine/samples/benchmark_app/README.md).
 
 ## Installation on Windows* OS
 
-For installation instructions, refer to the [Installation Guide for Windows*](../../install_guides/installing-openvino-windows.md).
+For installation instructions, refer to the [Installation Guide for Windows*](../../../inference-engine/samples/benchmark_app/README.md).
 
 ## Supported networks
 

docs/IE_DG/supported_plugins/Supported_Devices.md

@@ -11,6 +11,7 @@ The Inference Engine provides unique capabilities to infer deep learning models
 |------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------|
 |[GPU plugin](CL_DNN.md)            |Intel® Processor Graphics, including Intel® HD Graphics and Intel® Iris® Graphics                                                            |
 |[CPU plugin](CPU.md)              |Intel® Xeon® with Intel® Advanced Vector Extensions 2 (Intel® AVX2), Intel® Advanced Vector Extensions 512 (Intel® AVX-512), and AVX512_BF16, Intel® Core™ Processors with Intel® AVX2, Intel® Atom® Processors with Intel® Streaming SIMD Extensions (Intel® SSE) |
+|[FPGA plugin](FPGA.md) (available in the Intel® Distribution of OpenVINO™ toolkit)           |Intel® Vision Accelerator Design with an Intel® Arria 10 FPGA (Speed Grade 2), Intel® Programmable Acceleration Card with Intel® Arria® 10 GX FPGA |
 |[VPU plugins](VPU.md) (available in the Intel® Distribution of OpenVINO™ toolkit)            |Intel® Neural Compute Stick 2 powered by the Intel® Movidius™ Myriad™ X, Intel® Vision Accelerator Design with Intel® Movidius™ VPUs                                                                                           |
 |[GNA plugin](GNA.md) (available in the Intel® Distribution of OpenVINO™ toolkit)              |Intel® Speech Enabling Developer Kit, Amazon Alexa* Premium Far-Field Developer Kit, Intel® Pentium® Silver J5005 Processor, Intel® Pentium® Silver N5000 Processor, Intel® Celeron® J4005 Processor, Intel® Celeron® J4105 Processor, Intel® Celeron® Processor N4100, Intel® Celeron® Processor N4000, Intel® Core™ i3-8121U Processor, Intel® Core™ i7-1065G7 Processor, Intel® Core™ i7-1060G7 Processor, Intel® Core™ i5-1035G4 Processor, Intel® Core™ i5-1035G7 Processor, Intel® Core™ i5-1035G1 Processor, Intel® Core™ i5-1030G7 Processor, Intel® Core™ i5-1030G4 Processor, Intel® Core™ i3-1005G1 Processor, Intel® Core™ i3-1000G1 Processor, Intel® Core™ i3-1000G4 Processor|
 |[Multi-Device plugin](MULTI.md) |Multi-Device plugin enables simultaneous inference of the same network on several Intel® devices in parallel    |   
@@ -52,6 +53,7 @@ For example, the CHW value at index (c,h,w) is physically located at index (c\*H
 |:-------------|:----------------------:|:----------------------:|:----------------------:|
 |CPU plugin    |Supported and preferred |Supported               |Supported               |
 |GPU plugin    |Supported               |Supported and preferred |Supported\*             |
+|FPGA plugin   |Supported               |Supported               |Not supported           |
 |VPU plugins   |Not supported           |Supported               |Not supported           |
 |GNA plugin    |Supported               |Supported               |Not supported           |
 <br>\* - currently, only limited set of topologies might benefit from enabling I8 model on GPU<br>
@@ -64,6 +66,7 @@ the supported models formats depends on the actual underlying devices. _Generall
 |:-------------|:--------:|:-------------:|:-------------:|:-------------:|:------------:|:-------------:|
 |CPU plugin    |Supported |Not supported  |Supported      |Supported      |Not supported |Supported      |
 |GPU plugin    |Supported |Supported\*    |Supported\*    |Supported\*    |Not supported |Supported\*    |
+|FPGA plugin   |Supported |Supported\*    |Supported      |Supported      |Not supported |Supported      |
 |VPU plugins   |Supported |Supported      |Supported      |Not supported  |Not supported |Not supported  |
 |GNA plugin    |Supported |Not supported  |Supported      |Not supported  |Supported     |Supported      |
 
@@ -77,6 +80,7 @@ the supported input precision  depends on the actual underlying devices. _Genera
 |:-------------|:--------:|:------------:|
 |CPU plugin    |Supported |Not supported |
 |GPU plugin    |Supported |Supported     |
+|FPGA plugin   |Supported |Supported     |
 |VPU plugins   |Supported |Supported     |
 |GNA plugin    |Supported |Not supported |
 For [Multi-Device](MULTI.md) and [Heterogeneous](HETERO.md) execution
@@ -88,8 +92,9 @@ the supported output precision  depends on the actual underlying devices. _Gener
 |:-------------|:------------:|:------------:|:------------:|:------------:|
 |CPU plugin    |Supported     |Supported     |Supported     |Supported     |
 |GPU plugin    |Supported     |Supported     |Supported     |Supported     |
+|FPGA plugin   |Not supported |Supported     |Supported     |Not supported |
 |VPU plugins   |Not supported |Supported     |Supported     |Supported     |
-|GNA plugin    |Not supported |Supported     |Supported     |Supported     |
+|GNA plugin    |Not supported |Not supported |Not supported |Supported     |
 
 ### Supported Output Layout
 
@@ -104,152 +109,152 @@ For setting relevant configuration, refer to the
 ### Supported Layers
 The following layers are supported by the plugins and by [Shape Inference feature](../ShapeInference.md):
 
-| Layers                         | GPU           | CPU           | VPU           | GNA           | ShapeInfer    |
-|:-------------------------------|:-------------:|:-------------:|:-------------:|:-------------:|:-------------:|
-| Abs                            | Supported     | Supported\*\* | Supported     | Not Supported | Supported     |
-| Acos                           | Supported     | Supported\*\* | Not Supported | Not Supported | Supported     |
-| Acosh                          | Supported     | Supported\*\* | Not Supported | Not Supported | Supported     |
-| Activation-Clamp               | Supported     |Supported\*\*\*| Supported     | Supported     | Supported     |
-| Activation-ELU                 | Supported     |Supported\*\*\*| Supported     | Not Supported | Supported     |
-| Activation-Exp                 | Supported     |Supported\*\*\*| Not Supported | Supported     | Supported     |
-| Activation-Leaky ReLU          | Supported     |Supported\*\*\*| Supported     | Supported     | Supported     |
-| Activation-Not                 | Supported     |Supported\*\*\*| Not Supported | Not Supported | Supported     |
-| Activation-PReLU               | Supported     |Supported\*\*\*| Supported     | Not Supported | Supported     |
-| Activation-ReLU                | Supported     |Supported\*\*\*| Supported     | Supported     | Supported     |
-| Activation-ReLU6               | Supported     |Supported\*\*\*| Supported     | Not Supported | Supported     |
-| Activation-Sigmoid/Logistic    | Supported     |Supported\*\*\*| Supported     | Supported     | Supported     |
-| Activation-TanH                | Supported     |Supported\*\*\*| Supported     | Supported     | Supported     |
-| ArgMax                         | Supported     | Supported\*\* | Supported     | Not Supported | Supported     |
-| Asin                           | Supported     | Supported\*\* | Not Supported | Not Supported | Supported     |
-| Asinh                          | Supported     | Supported\*\* | Not Supported | Not Supported | Supported     |
-| Atan                           | Supported     | Supported\*\* | Not Supported | Not Supported | Supported     |
-| Atanh                          | Supported     | Supported\*\* | Not Supported | Not Supported | Supported     |
-| BatchNormalization             | Supported     | Supported     | Supported     | Not Supported | Supported     |
-| BinaryConvolution              | Supported     | Supported     | Not Supported | Not Supported | Supported     |
-| Broadcast                      | Supported     | Supported\*\* | Supported     | Not Supported | Supported     |
-| Ceil                           | Supported     | Supported\*\* | Not Supported | Not Supported | Supported     |
-| Concat                         | Supported     |Supported\*\*\*| Supported     | Supported     | Supported     |
-| Const                          | Supported     | Supported     | Supported     | Supported     | Not Supported |
-| Convolution-Dilated            | Supported     | Supported     | Supported     | Not Supported | Supported     |
-| Convolution-Dilated 3D         | Supported     | Supported     | Not Supported | Not Supported | Not Supported |
-| Convolution-Grouped            | Supported     | Supported     | Supported     | Not Supported | Supported     |
-| Convolution-Grouped 3D         | Supported     | Supported     | Not Supported | Not Supported | Not Supported |
-| Convolution-Ordinary           | Supported     | Supported     | Supported     | Supported\*   | Supported     |
-| Convolution-Ordinary 3D        | Supported     | Supported     | Not Supported | Not Supported | Not Supported |
-| Cos                            | Supported     | Supported\*\* | Not Supported | Not Supported | Supported     |
-| Cosh                           | Supported     | Supported\*\* | Not Supported | Not Supported | Supported     |
-| Crop                           | Supported     | Supported     | Supported     | Supported     | Supported     |
-| CTCGreedyDecoder               | Supported\*\* | Supported\*\* | Supported\*   | Not Supported | Supported     |
-| Deconvolution                  | Supported     | Supported     | Supported     | Not Supported | Supported     |
-| Deconvolution 3D               | Supported     | Supported     | Not Supported | Not Supported | Not Supported |
-| DeformableConvolution          | Supported     | Supported     | Not Supported | Not Supported | Supported     |
-| DepthToSpace                   | Supported     | Supported\*\* | Not Supported | Not Supported | Supported     |
-| DetectionOutput                | Supported     | Supported\*\* | Supported\*   | Not Supported | Supported     |
-| Eltwise-And                    | Supported     |Supported\*\*\*| Not Supported | Not Supported | Supported     |
-| Eltwise-Add                    | Supported     |Supported\*\*\*| Not Supported | Not Supported | Supported     |
-| Eltwise-Div                    | Supported     |Supported\*\*\*| Supported     | Not Supported | Supported     |
-| Eltwise-Equal                  | Supported     |Supported\*\*\*| Supported     | Not Supported | Supported     |
-| Eltwise-FloorMod               | Supported     |Supported\*\*\*| Supported     | Not Supported | Supported     |
-| Eltwise-Greater                | Supported     |Supported\*\*\*| Supported     | Not Supported | Supported     |
-| Eltwise-GreaterEqual           | Supported     |Supported\*\*\*| Supported     | Not Supported | Supported     |
-| Eltwise-Less                   | Supported     |Supported\*\*\*| Supported     | Not Supported | Supported     |
-| Eltwise-LessEqual              | Supported     |Supported\*\*\*| Supported     | Not Supported | Supported     |
-| Eltwise-LogicalAnd             | Supported     |Supported\*\*\*| Supported     | Not Supported | Supported     |
-| Eltwise-LogicalOr              | Supported     |Supported\*\*\*| Supported     | Not Supported | Supported     |
-| Eltwise-LogicalXor             | Supported     |Supported\*\*\*| Supported     | Not Supported | Supported     |
-| Eltwise-Max                    | Supported     |Supported\*\*\*| Supported     | Not Supported | Supported     |
-| Eltwise-Min                    | Supported     |Supported\*\*\*| Supported     | Not Supported | Supported     |
-| Eltwise-Mul                    | Supported     |Supported\*\*\*| Supported     | Supported     | Supported     |
-| Eltwise-NotEqual               | Supported     |Supported\*\*\*| Supported     | Not Supported | Supported     |
-| Eltwise-Pow                    | Supported     |Supported\*\*\*| Supported     | Not Supported | Supported     |
-| Eltwise-Prod                   | Supported     |Supported\*\*\*| Supported     | Supported     | Supported     |
-| Eltwise-SquaredDiff            | Supported     |Supported\*\*\*| Supported     | Not Supported | Supported     |
-| Eltwise-Sub                    | Supported     |Supported\*\*\*| Supported     | Supported     | Supported     |
-| Eltwise-Sum                    | Supported     |Supported\*\*\*| Supported     | Supported     | Supported     |
-| Erf                            | Supported     | Supported\*\* | Not Supported | Not Supported | Supported     |
-| Exp                            | Supported     | Supported     | Not Supported | Supported     | Supported     |
-| FakeQuantize                   | Not Supported | Supported     | Not Supported | Not Supported | Supported     |
-| Fill                           | Not Supported | Supported\*\* | Not Supported | Not Supported | Supported     |
-| Flatten                        | Supported     | Supported     | Supported     | Not Supported | Supported     |
-| Floor                          | Supported     | Supported\*\* | Not Supported | Not Supported | Supported     |
-| FullyConnected (Inner Product) | Supported     |Supported\*\*\*| Supported     | Supported     | Supported     |
-| Gather                         | Supported     | Supported\*\* | Supported     | Not Supported | Supported     |
-| GatherTree                     | Not Supported | Supported\*\* | Not Supported | Not Supported | Supported     |
-| Gemm                           | Supported     | Supported     | Supported     | Not Supported | Supported     |
-| GRN                            | Supported\*\* | Supported\*\* | Supported     | Not Supported | Supported     |
-| HardSigmoid                    | Supported     | Supported\*\* | Not Supported | Not Supported | Supported     |
-| Interp                         | Supported\*\* | Supported\*\* | Supported     | Not Supported | Supported\*   |
-| Log                            | Supported     | Supported\*\* | Supported     | Supported     | Supported     |
-| LRN (Norm)                     | Supported     | Supported     | Supported     | Not Supported | Supported     |
-| LSTMCell                       | Supported     | Supported     | Supported     | Supported     | Not Supported |
-| GRUCell                        | Supported     | Supported     | Not Supported | Not Supported | Not Supported |
-| RNNCell                        | Supported     | Supported     | Not Supported | Not Supported | Not Supported |
-| LSTMSequence                   | Supported     | Supported     | Supported     | Not Supported | Not Supported |
-| GRUSequence                    | Supported     | Supported     | Not Supported | Not Supported | Not Supported |
-| RNNSequence                    | Supported     | Supported     | Not Supported | Not Supported | Not Supported |
-| LogSoftmax                     | Supported     | Supported\*\* | Not Supported | Not Supported | Not Supported |
-| Memory                         | Not Supported | Supported     | Not Supported | Supported     | Supported     |
-| MVN                            | Supported     | Supported\*\* | Supported\*   | Not Supported | Supported     |
-| Neg                            | Supported     | Supported\*\* | Not Supported | Not Supported | Supported     |
-| NonMaxSuppression              | Not Supported | Supported\*\* | Not Supported | Not Supported | Supported     |
-| Normalize                      | Supported     | Supported\*\* | Supported\*   | Not Supported | Supported     |
-| OneHot                         | Supported     | Supported\*\* | Not Supported | Not Supported | Supported     |
-| Pad                            | Supported     | Supported\*\* | Supported\*   | Not Supported | Supported     |
-| Permute                        | Supported     | Supported     | Supported     | Supported\*   | Supported     |
-| Pooling(AVG,MAX)               | Supported     | Supported     | Supported     | Supported     | Supported     |
-| Pooling(AVG,MAX) 3D            | Supported     | Supported     | Not Supported | Not Supported | Not Supported |
-| Power                          | Supported     | Supported\*\* | Supported     | Supported\*   | Supported     |
-| PowerFile                      | Not Supported | Supported\*\* | Not Supported | Not Supported | Not Supported |
-| PriorBox                       | Supported     | Supported\*\* | Supported     | Not Supported | Supported     |
-| PriorBoxClustered              | Supported\*\* | Supported\*\* | Supported     | Not Supported | Supported     |
-| Proposal                       | Supported     | Supported\*\* | Supported     | Not Supported | Supported     |
-| PSROIPooling                   | Supported     | Supported\*\* | Supported     | Not Supported | Supported     |
-| Range                          | Not Supported | Supported\*\* | Not Supported | Not Supported | Supported     |
-| Reciprocal                     | Supported     | Supported\*\* | Not Supported | Not Supported | Supported     |
-| ReduceAnd                      | Supported     | Supported\*\* | Not Supported | Not Supported | Supported     |
-| ReduceL1                       | Supported     | Supported\*\* | Not Supported | Not Supported | Supported     |
-| ReduceL2                       | Supported     | Supported\*\* | Not Supported | Not Supported | Supported     |
-| ReduceLogSum                   | Supported     | Supported\*\* | Not Supported | Not Supported | Supported     |
-| ReduceLogSumExp                | Supported     | Supported\*\* | Not Supported | Not Supported | Supported     |
-| ReduceMax                      | Supported     | Supported\*\* | Not Supported | Not Supported | Supported     |
-| ReduceMean                     | Supported     | Supported\*\* | Not Supported | Not Supported | Supported     |
-| ReduceMin                      | Supported     | Supported\*\* | Not Supported | Not Supported | Supported     |
-| ReduceOr                       | Supported     | Supported\*\* | Not Supported | Not Supported | Supported     |
-| ReduceProd                     | Supported     | Supported\*\* | Not Supported | Not Supported | Supported     |
-| ReduceSum                      | Supported     | Supported\*\* | Not Supported | Not Supported | Supported     |
-| ReduceSumSquare                | Supported     | Supported\*\* | Not Supported | Not Supported | Supported     |
-| RegionYolo                     | Supported     | Supported\*\* | Supported     | Not Supported | Supported     |
-| ReorgYolo                      | Supported     | Supported\*\* | Supported     | Not Supported | Supported     |
-| Resample                       | Supported     | Supported\*\* | Supported     | Not Supported | Supported     |
-| Reshape                        | Supported     |Supported\*\*\*| Supported     | Supported     | Supported\*   |
-| ReverseSequence                | Supported     | Supported\*\* | Supported     | Not Supported | Supported     |
-| RNN                            | Not Supported | Supported     | Supported     | Not Supported | Not Supported |
-| ROIPooling                     | Supported\*   | Supported     | Supported     | Not Supported | Supported     |
-| ScaleShift                     | Supported     |Supported\*\*\*| Supported\*   | Supported     | Supported     |
-| ScatterUpdate                  | Not Supported | Supported\*\* | Not Supported | Not Supported | Supported     |
-| Select                         | Supported     | Supported     | Supported     | Not Supported | Supported     |
-| Selu                           | Supported     | Supported\*\* | Not Supported | Not Supported | Supported     |
-| ShuffleChannels                | Supported     | Supported\*\* | Not Supported | Not Supported | Supported     |
-| Sign                           | Supported     | Supported\*\* | Supported     | Not Supported | Supported     |
-| Sin                            | Supported     | Supported\*\* | Not Supported | Not Supported | Supported     |
-| Sinh                           | Supported     | Supported\*\* | Not Supported | Not Supported | Supported     |
-| SimplerNMS                     | Supported     | Supported\*\* | Not Supported | Not Supported | Supported     |
-| Slice                          | Supported     |Supported\*\*\*| Supported     | Supported     | Supported     |
-| SoftMax                        | Supported     |Supported\*\*\*| Supported     | Not Supported | Supported     |
-| Softplus                       | Supported     | Supported\*\* | Not Supported | Not Supported | Supported     |
-| Softsign                       | Supported     | Supported\*\* | Not Supported | Supported     | Supported     |
-| SpaceToDepth                   | Not Supported | Supported\*\* | Not Supported | Not Supported | Supported     |
-| SpatialTransformer             | Not Supported | Supported\*\* | Not Supported | Not Supported | Supported     |
-| Split                          | Supported     |Supported\*\*\*| Supported     | Supported     | Supported     |
-| Squeeze                        | Supported     | Supported\*\* | Supported     | Supported     | Supported     |
-| StridedSlice                   | Supported     | Supported\*\* | Not Supported | Not Supported | Supported     |
-| Tan                            | Supported     | Supported\*\* | Not Supported | Not Supported | Supported     |
-| TensorIterator                 | Not Supported | Supported     | Supported     | Supported     | Not Supported |
-| Tile                           | Supported\*\* |Supported\*\*\*| Supported     | Not Supported | Supported     |
-| TopK                           | Supported     | Supported\*\* | Not Supported | Not Supported | Supported     |
-| Unpooling                      | Supported     | Not Supported | Not Supported | Not Supported | Not Supported |
-| Unsqueeze                      | Supported     | Supported\*\* | Supported     | Supported     | Supported     |
-| Upsampling                     | Supported     | Not Supported | Not Supported | Not Supported | Not Supported |
+| Layers                         | GPU           | CPU           | VPU           | GNA           | FPGA            | ShapeInfer    |
+|:-------------------------------|:-------------:|:-------------:|:-------------:|:-------------:|:---------------:|:-------------:|
+| Abs                            | Supported     | Supported\*\* | Supported     | Not Supported | Not Supported   | Supported     |
+| Acos                           | Supported     | Supported\*\* | Not Supported | Not Supported | Not Supported   | Supported     |
+| Acosh                          | Supported     | Supported\*\* | Not Supported | Not Supported | Not Supported   | Supported     |
+| Activation-Clamp               | Supported     |Supported\*\*\*| Supported     | Supported     | Supported       | Supported     |
+| Activation-ELU                 | Supported     |Supported\*\*\*| Supported     | Not Supported | Supported       | Supported     |
+| Activation-Exp                 | Supported     |Supported\*\*\*| Not Supported | Supported     | Not Supported   | Supported     |
+| Activation-Leaky ReLU          | Supported     |Supported\*\*\*| Supported     | Supported     | Supported       | Supported     |
+| Activation-Not                 | Supported     |Supported\*\*\*| Not Supported | Not Supported | Not Supported   | Supported     |
+| Activation-PReLU               | Supported     |Supported\*\*\*| Supported     | Not Supported | Supported       | Supported     |
+| Activation-ReLU                | Supported     |Supported\*\*\*| Supported     | Supported     | Supported       | Supported     |
+| Activation-ReLU6               | Supported     |Supported\*\*\*| Supported     | Not Supported | Not Supported   | Supported     |
+| Activation-Sigmoid/Logistic    | Supported     |Supported\*\*\*| Supported     | Supported     | Not Supported   | Supported     |
+| Activation-TanH                | Supported     |Supported\*\*\*| Supported     | Supported     | Not Supported   | Supported     |
+| ArgMax                         | Supported     | Supported\*\* | Supported     | Not Supported | Not Supported   | Supported     |
+| Asin                           | Supported     | Supported\*\* | Not Supported | Not Supported | Not Supported   | Supported     |
+| Asinh                          | Supported     | Supported\*\* | Not Supported | Not Supported | Not Supported   | Supported     |
+| Atan                           | Supported     | Supported\*\* | Not Supported | Not Supported | Not Supported   | Supported     |
+| Atanh                          | Supported     | Supported\*\* | Not Supported | Not Supported | Not Supported   | Supported     |
+| BatchNormalization             | Supported     | Supported     | Supported     | Not Supported | Supported\*     | Supported     |
+| BinaryConvolution              | Supported     | Supported     | Not Supported | Not Supported | Not Supported   | Supported     |
+| Broadcast                      | Supported     | Supported\*\* | Supported     | Not Supported | Not Supported   | Supported     |
+| Ceil                           | Supported     | Supported\*\* | Not Supported | Not Supported | Not Supported   | Supported     |
+| Concat                         | Supported     |Supported\*\*\*| Supported     | Supported     | Supported       | Supported     |
+| Const                          | Supported     | Supported     | Supported     | Supported     | Not Supported   | Not Supported |
+| Convolution-Dilated            | Supported     | Supported     | Supported     | Not Supported | Supported       | Supported     |
+| Convolution-Dilated 3D         | Supported     | Supported     | Not Supported | Not Supported | Not Supported   | Not Supported |
+| Convolution-Grouped            | Supported     | Supported     | Supported     | Not Supported | Supported       | Supported     |
+| Convolution-Grouped 3D         | Supported     | Supported     | Not Supported | Not Supported | Not Supported   | Not Supported |
+| Convolution-Ordinary           | Supported     | Supported     | Supported     | Supported\*   | Supported       | Supported     |
+| Convolution-Ordinary 3D        | Supported     | Supported     | Not Supported | Not Supported | Not Supported   | Not Supported |
+| Cos                            | Supported     | Supported\*\* | Not Supported | Not Supported | Not Supported   | Supported     |
+| Cosh                           | Supported     | Supported\*\* | Not Supported | Not Supported | Not Supported   | Supported     |
+| Crop                           | Supported     | Supported     | Supported     | Supported     | Not Supported   | Supported     |
+| CTCGreedyDecoder               | Supported\*\* | Supported\*\* | Supported\*   | Not Supported | Not Supported   | Supported     |
+| Deconvolution                  | Supported     | Supported     | Supported     | Not Supported | Supported\*     | Supported     |
+| Deconvolution 3D               | Supported     | Supported     | Not Supported | Not Supported | Not Supported   | Not Supported |
+| DeformableConvolution          | Supported     | Supported     | Not Supported | Not Supported | Not Supported   | Supported     |
+| DepthToSpace                   | Supported     | Supported\*\* | Not Supported | Not Supported | Not Supported   | Supported     |
+| DetectionOutput                | Supported     | Supported\*\* | Supported\*   | Not Supported | Not Supported   | Supported     |
+| Eltwise-And                    | Supported     |Supported\*\*\*| Not Supported | Not Supported | Not Supported   | Supported     |
+| Eltwise-Add                    | Supported     |Supported\*\*\*| Not Supported | Not Supported | Supported       | Supported     |
+| Eltwise-Div                    | Supported     |Supported\*\*\*| Supported     | Not Supported | Not Supported   | Supported     |
+| Eltwise-Equal                  | Supported     |Supported\*\*\*| Supported     | Not Supported | Not Supported   | Supported     |
+| Eltwise-FloorMod               | Supported     |Supported\*\*\*| Supported     | Not Supported | Not Supported   | Supported     |
+| Eltwise-Greater                | Supported     |Supported\*\*\*| Supported     | Not Supported | Not Supported   | Supported     |
+| Eltwise-GreaterEqual           | Supported     |Supported\*\*\*| Supported     | Not Supported | Not Supported   | Supported     |
+| Eltwise-Less                   | Supported     |Supported\*\*\*| Supported     | Not Supported | Not Supported   | Supported     |
+| Eltwise-LessEqual              | Supported     |Supported\*\*\*| Supported     | Not Supported | Not Supported   | Supported     |
+| Eltwise-LogicalAnd             | Supported     |Supported\*\*\*| Supported     | Not Supported | Not Supported   | Supported     |
+| Eltwise-LogicalOr              | Supported     |Supported\*\*\*| Supported     | Not Supported | Not Supported   | Supported     |
+| Eltwise-LogicalXor             | Supported     |Supported\*\*\*| Supported     | Not Supported | Not Supported   | Supported     |
+| Eltwise-Max                    | Supported     |Supported\*\*\*| Supported     | Not Supported | Not Supported   | Supported     |
+| Eltwise-Min                    | Supported     |Supported\*\*\*| Supported     | Not Supported | Not Supported   | Supported     |
+| Eltwise-Mul                    | Supported     |Supported\*\*\*| Supported     | Supported     | Not Supported   | Supported     |
+| Eltwise-NotEqual               | Supported     |Supported\*\*\*| Supported     | Not Supported | Not Supported   | Supported     |
+| Eltwise-Pow                    | Supported     |Supported\*\*\*| Supported     | Not Supported | Not Supported   | Supported     |
+| Eltwise-Prod                   | Supported     |Supported\*\*\*| Supported     | Supported     | Not Supported   | Supported     |
+| Eltwise-SquaredDiff            | Supported     |Supported\*\*\*| Supported     | Not Supported | Not Supported   | Supported     |
+| Eltwise-Sub                    | Supported     |Supported\*\*\*| Supported     | Supported     | Supported       | Supported     |
+| Eltwise-Sum                    | Supported     |Supported\*\*\*| Supported     | Supported     | Supported       | Supported     |
+| Erf                            | Supported     | Supported\*\* | Not Supported | Not Supported | Not Supported   | Supported     |
+| Exp                            | Supported     | Supported     | Not Supported | Supported     | Not Supported   | Supported     |
+| FakeQuantize                   | Not Supported | Supported     | Not Supported | Not Supported | Not Supported   | Supported     |
+| Fill                           | Not Supported | Supported\*\* | Not Supported | Not Supported | Not Supported   | Supported     |
+| Flatten                        | Supported     | Supported     | Supported     | Not Supported | Not Supported   | Supported     |
+| Floor                          | Supported     | Supported\*\* | Not Supported | Not Supported | Not Supported   | Supported     |
+| FullyConnected (Inner Product) | Supported     |Supported\*\*\*| Supported     | Supported     | Supported       | Supported     |
+| Gather                         | Supported     | Supported\*\* | Supported     | Not Supported | Not Supported   | Supported     |
+| GatherTree                     | Not Supported | Supported\*\* | Not Supported | Not Supported | Not Supported   | Supported     |
+| Gemm                           | Supported     | Supported     | Supported     | Not Supported | Not Supported   | Supported     |
+| GRN                            | Supported\*\* | Supported\*\* | Supported     | Not Supported | Not Supported   | Supported     |
+| HardSigmoid                    | Supported     | Supported\*\* | Not Supported | Not Supported | Not Supported   | Supported     |
+| Interp                         | Supported\*\* | Supported\*\* | Supported     | Not Supported | Not Supported   | Supported\*   |
+| Log                            | Supported     | Supported\*\* | Supported     | Supported     | Not Supported   | Supported     |
+| LRN (Norm)                     | Supported     | Supported     | Supported     | Not Supported | Supported       | Supported     |
+| LSTMCell                       | Supported     | Supported     | Supported     | Supported     | Not Supported   | Not Supported |
+| GRUCell                        | Supported     | Supported     | Not Supported | Not Supported | Not Supported   | Not Supported |
+| RNNCell                        | Supported     | Supported     | Not Supported | Not Supported | Not Supported   | Not Supported |
+| LSTMSequence                   | Supported     | Supported     | Supported     | Not Supported | Not Supported   | Not Supported |
+| GRUSequence                    | Supported     | Supported     | Not Supported | Not Supported | Not Supported   | Not Supported |
+| RNNSequence                    | Supported     | Supported     | Not Supported | Not Supported | Not Supported   | Not Supported |
+| LogSoftmax                     | Supported     | Supported\*\* | Not Supported | Not Supported | Not Supported   | Not Supported |
+| Memory                         | Not Supported | Supported     | Not Supported | Supported     | Not Supported   | Supported     |
+| MVN                            | Supported     | Supported\*\* | Supported\*   | Not Supported | Not Supported   | Supported     |
+| Neg                            | Supported     | Supported\*\* | Not Supported | Not Supported | Not Supported   | Supported     |
+| NonMaxSuppression              | Not Supported | Supported\*\* | Not Supported | Not Supported | Not Supported   | Supported     |
+| Normalize                      | Supported     | Supported\*\* | Supported\*   | Not Supported | Not Supported   | Supported     |
+| OneHot                         | Supported     | Supported\*\* | Not Supported | Not Supported | Not Supported   | Supported     |
+| Pad                            | Supported     | Supported\*\* | Supported\*   | Not Supported | Not Supported   | Supported     |
+| Permute                        | Supported     | Supported     | Supported     | Supported\*   | Not Supported   | Supported     |
+| Pooling(AVG,MAX)               | Supported     | Supported     | Supported     | Supported     | Supported       | Supported     |
+| Pooling(AVG,MAX) 3D            | Supported     | Supported     | Not Supported | Not Supported | Not Supported   | Not Supported |
+| Power                          | Supported     | Supported\*\* | Supported     | Supported\*   | Supported\*     | Supported     |
+| PowerFile                      | Not Supported | Supported\*\* | Not Supported | Not Supported | Not Supported   | Not Supported |
+| PriorBox                       | Supported     | Supported\*\* | Supported     | Not Supported | Not Supported   | Supported     |
+| PriorBoxClustered              | Supported\*\* | Supported\*\* | Supported     | Not Supported | Not Supported   | Supported     |
+| Proposal                       | Supported     | Supported\*\* | Supported     | Not Supported | Not Supported   | Supported     |
+| PSROIPooling                   | Supported     | Supported\*\* | Supported     | Not Supported | Not Supported   | Supported     |
+| Range                          | Not Supported | Supported\*\* | Not Supported | Not Supported | Not Supported   | Supported     |
+| Reciprocal                     | Supported     | Supported\*\* | Not Supported | Not Supported | Not Supported   | Supported     |
+| ReduceAnd                      | Supported     | Supported\*\* | Not Supported | Not Supported | Not Supported   | Supported     |
+| ReduceL1                       | Supported     | Supported\*\* | Not Supported | Not Supported | Not Supported   | Supported     |
+| ReduceL2                       | Supported     | Supported\*\* | Not Supported | Not Supported | Not Supported   | Supported     |
+| ReduceLogSum                   | Supported     | Supported\*\* | Not Supported | Not Supported | Not Supported   | Supported     |
+| ReduceLogSumExp                | Supported     | Supported\*\* | Not Supported | Not Supported | Not Supported   | Supported     |
+| ReduceMax                      | Supported     | Supported\*\* | Not Supported | Not Supported | Not Supported   | Supported     |
+| ReduceMean                     | Supported     | Supported\*\* | Not Supported | Not Supported | Not Supported   | Supported     |
+| ReduceMin                      | Supported     | Supported\*\* | Not Supported | Not Supported | Not Supported   | Supported     |
+| ReduceOr                       | Supported     | Supported\*\* | Not Supported | Not Supported | Not Supported   | Supported     |
+| ReduceProd                     | Supported     | Supported\*\* | Not Supported | Not Supported | Not Supported   | Supported     |
+| ReduceSum                      | Supported     | Supported\*\* | Not Supported | Not Supported | Not Supported   | Supported     |
+| ReduceSumSquare                | Supported     | Supported\*\* | Not Supported | Not Supported | Not Supported   | Supported     |
+| RegionYolo                     | Supported     | Supported\*\* | Supported     | Not Supported | Not Supported   | Supported     |
+| ReorgYolo                      | Supported     | Supported\*\* | Supported     | Not Supported | Not Supported   | Supported     |
+| Resample                       | Supported     | Supported\*\* | Supported     | Not Supported | Supported\*     | Supported     |
+| Reshape                        | Supported     |Supported\*\*\*| Supported     | Supported     | Not Supported   | Supported\*   |
+| ReverseSequence                | Supported     | Supported\*\* | Supported     | Not Supported | Not Supported   | Supported     |
+| RNN                            | Not Supported | Supported     | Supported     | Not Supported | Not Supported   | Not Supported |
+| ROIPooling                     | Supported\*   | Supported     | Supported     | Not Supported | Not Supported   | Supported     |
+| ScaleShift                     | Supported     |Supported\*\*\*| Supported\*   | Supported     | Supported       | Supported     |
+| ScatterUpdate                  | Not Supported | Supported\*\* | Not Supported | Not Supported | Not Supported   | Supported     |
+| Select                         | Supported     | Supported     | Supported     | Not Supported | Not Supported   | Supported     |
+| Selu                           | Supported     | Supported\*\* | Not Supported | Not Supported | Not Supported   | Supported     |
+| ShuffleChannels                | Supported     | Supported\*\* | Not Supported | Not Supported | Not Supported   | Supported     |
+| Sign                           | Supported     | Supported\*\* | Supported     | Not Supported | Not Supported   | Supported     |
+| Sin                            | Supported     | Supported\*\* | Not Supported | Not Supported | Not Supported   | Supported     |
+| Sinh                           | Supported     | Supported\*\* | Not Supported | Not Supported | Not Supported   | Supported     |
+| SimplerNMS                     | Supported     | Supported\*\* | Not Supported | Not Supported | Not Supported   | Supported     |
+| Slice                          | Supported     |Supported\*\*\*| Supported     | Supported     | Supported\*     | Supported     |
+| SoftMax                        | Supported     |Supported\*\*\*| Supported     | Not Supported | Not Supported   | Supported     |
+| Softplus                       | Supported     | Supported\*\* | Not Supported | Not Supported | Not Supported   | Supported     |
+| Softsign                       | Supported     | Supported\*\* | Not Supported | Supported     | Not Supported   | Supported     |
+| SpaceToDepth                   | Not Supported | Supported\*\* | Not Supported | Not Supported | Not Supported   | Supported     |
+| SpatialTransformer             | Not Supported | Supported\*\* | Not Supported | Not Supported | Not Supported   | Supported     |
+| Split                          | Supported     |Supported\*\*\*| Supported     | Supported     | Supported\*     | Supported     |
+| Squeeze                        | Supported     | Supported\*\* | Supported     | Supported     | Not Supported   | Supported     |
+| StridedSlice                   | Supported     | Supported\*\* | Not Supported | Not Supported | Not Supported   | Supported     |
+| Tan                            | Supported     | Supported\*\* | Not Supported | Not Supported | Not Supported   | Supported     |
+| TensorIterator                 | Not Supported | Supported     | Supported     | Supported     | Not Supported   | Not Supported |
+| Tile                           | Supported\*\* |Supported\*\*\*| Supported     | Not Supported | Not Supported   | Supported     |
+| TopK                           | Supported     | Supported\*\* | Not Supported | Not Supported | Not Supported   | Supported     |
+| Unpooling                      | Supported     | Not Supported | Not Supported | Not Supported | Not Supported   | Not Supported |
+| Unsqueeze                      | Supported     | Supported\*\* | Supported     | Supported     | Not Supported   | Supported     |
+| Upsampling                     | Supported     | Not Supported | Not Supported | Not Supported | Not Supported   | Not Supported |
 
 \*- support is limited to the specific parameters. Refer to "Known Layers Limitation" section for the device [from the list of supported](Supported_Devices.md).
 

docs/IE_PLUGIN_DG/Building.md

@@ -16,24 +16,22 @@ $ cmake -DCMAKE_BUILD_TYPE=Release ../dldt
 Once the commands above are executed, the Inference Engine Developer Package is generated in the `dldt-release-build` folder. It consists of several files:
  - `InferenceEngineDeveloperPackageConfig.cmake` - the main CMake script which imports targets and provides compilation flags and CMake options.
  - `InferenceEngineDeveloperPackageConfig-version.cmake` - a file with a package version.
- - `targets_developer.cmake` - an automatically generated file which contains all targets exported from the OpenVINO build tree. This file is included by `InferenceEngineDeveloperPackageConfig.cmake` to import the following targets:
+ - `targets_developer.cmake` - an automatically generated file which contains all targets exported from the Deep Learning Deployment Toolkit (DLDT) build tree. This file is included by `InferenceEngineDeveloperPackageConfig.cmake` to import the following targets:
    - Libraries for plugin development:
-       * `IE::ngraph` - shared nGraph library
-       * `IE::inference_engine` - shared Inference Engine library
-       * `IE::inference_engine_transformations` - shared library with Inference Engine ngraph-based Transformations
-       * `IE::inference_engine_preproc` - shared library with Inference Engine preprocessing plugin
-       * `IE::inference_engine_plugin_api` - interface library with Inference Engine Plugin API headers
-       * `IE::inference_engine_lp_transformations` - shared library with low-precision transformations
-       * `IE::pugixml` - static Pugixml library
-       * `IE::xbyak` - interface library with Xbyak headers
-       * `IE::itt` - static library with tools for performance measurement using Intel ITT
+	   * `IE::ngraph` - shared nGraph library
+	   * `IE::inference_engine` - shared Inference Engine library
+	   * `IE::inference_engine_preproc` - shared library with Inference Engine preprocessing plugin
+	   * `IE::inference_engine_plugin_api` - interface library with Inference Engine Plugin API headers
+	   * `IE::inference_engine_lp_transformations` - shared library with low-precision transformations
+	   * `IE::pugixml` - static Pugixml library
+	   * `IE::xbyak` - interface library with Xbyak headers
    - Libraries for tests development:
-       * `IE::gtest`, `IE::gtest_main`, `IE::gmock` - Google Tests framework libraries
-       * `IE::commonTestUtils` - static library with common tests utilities 
-       * `IE::funcTestUtils` - static library with functional tests utilities 
-       * `IE::unitTestUtils` - static library with unit tests utilities 
-       * `IE::ngraphFunctions` - static library with the set of `ngraph::Function` builders
-       * `IE::funcSharedTests` - static library with common functional tests
+	   * `IE::gtest`, `IE::gtest_main`, `IE::gmock` - Google Tests framework libraries
+	   * `IE::commonTestUtils` - static library with common tests utilities 
+	   * `IE::funcTestUtils` - static library with functional tests utilities 
+	   * `IE::unitTestUtils` - static library with unit tests utilities 
+	   * `IE::ngraphFunctions` - static library with the set of Ngraph Functions builders
+	   * `IE::funcSharedTests` - static library with common functional tests
 
 > **Note:** it's enough just to run `cmake --build . --target ie_dev_targets` command to build only targets from the
 > Inference Engine Developer package.
@@ -70,20 +68,23 @@ find_package(InferenceEngineDeveloperPackage REQUIRED)
 add_subdirectory(src)
 
 if(ENABLE_TESTS)
-    include(CTest)
-    enable_testing()
+	include(CTest)
+	enable_testing()
 
-    if(ENABLE_FUNCTIONAL_TESTS)
-        add_subdirectory(tests/functional)
-    endif()
+	if(ENABLE_FUNCTIONAL_TESTS)
+	    add_subdirectory(tests/functional)
+	endif()
+
+	if(ENABLE_BEH_TESTS)
+	    add_subdirectory(tests/behavior)
+	endif()
 endif()
 ```
 
-> **NOTE**: The default values of the `ENABLE_TESTS`, `ENABLE_FUNCTIONAL_TESTS` options are shared via the Inference Engine Developer Package and they are the same as for the main DLDT build tree. You can override them during plugin build using the command below:
-
-    ```bash
-    $ cmake -DENABLE_FUNCTIONAL_TESTS=OFF -DInferenceEngineDeveloperPackage_DIR=../dldt-release-build ../template-plugin
-    ``` 
+> **NOTE**: The default values of the `ENABLE_TESTS`, `ENABLE_FUNCTIONAL_TESTS`, `ENABLE_BEH_TESTS` options are shared via the Inference Engine Developer Package and they are the same as for the main DLDT build tree. You can override them during plugin build using the command below:  
+	```bash
+	$ cmake -DENABLE_FUNCTIONAL_TESTS=OFF -DInferenceEngineDeveloperPackage_DIR=../dldt-release-build ../template-plugin
+	``` 
 
 - `src/CMakeLists.txt` to build a plugin shared library from sources:
 

docs/IE_PLUGIN_DG/Doxyfile

@@ -781,9 +781,7 @@ WARN_LOGFILE           =
 # Note: If this tag is empty the current directory is searched.
 
 INPUT                  = . \
-						 ../../inference-engine/src/transformations/include/transformations \
-						 ../../inference-engine/src/plugin_api \
-						 ../../openvino/itt/include/openvino
+						 ../../inference-engine/src/plugin_api
 
 # This tag can be used to specify the character encoding of the source files
 # that doxygen parses. Internally doxygen uses the UTF-8 encoding. Doxygen uses
@@ -848,9 +846,8 @@ EXCLUDE_PATTERNS       = cnn_network_ngraph_impl.hpp \
                          ie_imemory_state_internal.hpp \
                          ie_memory_state_internal.hpp \
                          ie_memory_state_base.hpp \
-                         generic_ie.hpp \
-                         function_name.hpp \
-                         macro_overload.hpp
+                         convert_function_to_cnn_network.hpp \
+                         generic_ie.hpp
 
 # The EXCLUDE_SYMBOLS tag can be used to specify one or more symbol names
 # (namespaces, classes, functions, etc.) that should be excluded from the
@@ -871,9 +868,6 @@ EXAMPLE_PATH           = ../template_plugin/src \
 						 ../template_plugin/include \
                          ../template_plugin/src/CMakeLists.txt \
                          ../template_plugin/tests/functional/CMakeLists.txt \
-                         ../template_plugin/tests/functional/transformations \
-                         ../template_plugin/tests/functional/shared_tests_instances/ \
-                         ../../inference-engine/tests/functional/plugin/shared/include \
                          ../examples
 
 # If the value of the EXAMPLE_PATH tag contains directories, you can use the
@@ -882,8 +876,7 @@ EXAMPLE_PATH           = ../template_plugin/src \
 # files are included.
 
 EXAMPLE_PATTERNS       = *.cpp \
-                         *.hpp \
-                         *.txt
+						 *.hpp
 
 # If the EXAMPLE_RECURSIVE tag is set to YES then subdirectories will be
 # searched for input files to be used with the \include or \dontinclude commands
@@ -896,7 +889,7 @@ EXAMPLE_RECURSIVE      = YES
 # that contain images that are to be included in the documentation (see the
 # \image command).
 
-IMAGE_PATH             = ./images
+IMAGE_PATH             =
 
 # The INPUT_FILTER tag can be used to specify a program that doxygen should
 # invoke to filter for each input file. Doxygen will invoke the filter program
@@ -2050,8 +2043,7 @@ INCLUDE_FILE_PATTERNS  =
 # recursively expanded use the := operator instead of the = operator.
 # This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
 
-PREDEFINED             = INFERENCE_PLUGIN_API \
-                         INFERENCE_ENGINE_API \
+PREDEFINED             = INFERENCE_ENGINE_API \
                          INFERENCE_ENGINE_API_CPP \
                          INFERENCE_ENGINE_API_CLASS \
                          INFERENCE_ENGINE_DEPRECATED \
@@ -2059,8 +2051,7 @@ PREDEFINED             = INFERENCE_PLUGIN_API \
                          IE_SUPPRESS_DEPRECATED_END \
                          IE_SUPPRESS_DEPRECATED_START_WIN \
                          IE_SUPPRESS_DEPRECATED_END_WIN \
-                         IE_THREAD=IE_THREAD_TBB \
-                         TRANSFORMATIONS_API
+                         IE_THREAD=IE_THREAD_TBB
 
 # If the MACRO_EXPANSION and EXPAND_ONLY_PREDEF tags are set to YES then this
 # tag can be used to specify a list of macro names that should be expanded. The

docs/IE_PLUGIN_DG/ExecutableNetwork.md

@@ -1,7 +1,7 @@
 # Executable Network {#executable_network}
 
 `ExecutableNetwork` class functionality:
-- Compile an InferenceEngine::ICNNNetwork instance to a backend specific graph representation
+- Compile an InferenceEngine::ICNNNetwork instance to a hardware-specific graph representation
 - Create an arbitrary number of `InferRequest` objects
 - Hold some common resources shared between different instances of `InferRequest`. For example:
 	- InferenceEngine::ExecutableNetworkInternal::_taskExecutor task executor to implement asynchronous execution
@@ -19,37 +19,36 @@ Inference Engine Plugin API provides the helper InferenceEngine::ExecutableNetwo
 The example class has several fields:
 
 - `_requestId` - Tracks a number of created inference requests, which is used to distinguish different inference requests during profiling via the Intel® Instrumentation and Tracing Technology (ITT) library.
+- `_name` - Provides a network name.
 - `_cfg` - Defines a configuration an executable network was compiled with.
 - `_plugin` - Refers to a plugin instance.
-- `_function` - Keeps a reference to transformed `ngraph::Function` which is used in ngraph reference backend computations. Note, in case of other backends with backend specific graph representation `_function` has different type and represents backend specific graph or just a set of computational kernels to perform an inference.
-- `_inputIndex` - maps a name of input with its index among all network inputs.
-- `_outputIndex` - maps a name of output with its index among all network outputs.
 
 ### `ExecutableNetwork` Constructor with `ICNNNetwork`
 
-This constructor accepts a generic representation of a neural network as an InferenceEngine::ICNNNetwork reference and is compiled into a backend specific device graph:
+This constructor accepts a generic representation of a neural network as an InferenceEngine::ICNNNetwork reference and is compiled into a hardware-specific device graph:
 
 @snippet src/template_executable_network.cpp executable_network:ctor_cnnnetwork
 
-The implementation `CompileNetwork` is fully device-specific.
+The implementation `CompileGraph` is fully device-specific.
 
-### `CompileNetwork()`
+### `CompileGraph()`
 
-The function accepts a const shared pointer to `ngraph::Function` object and performs the following steps:
+The function accepts a const shared pointer to `const ngraph::Function` object and performs the following steps:
 
-1. Applies ngraph passes using `TransformNetwork` function, which defines plugin-specific conversion pipeline. 
-2. Maps the transformed graph to a backend specific graph representation (for example, to MKLDNN graph for Intel CPU).
-3. Allocates and fills memory for graph weights, backend specific memory handles and so on.
+1. Deep copies a const object to a local object, which can later be modified.
+2. Applies common and plugin-specific transformations on a copied graph to make the graph more friendly to hardware operations.
+3. Maps the transformed graph to a plugin-specific graph representation (for example, to MKLDNN graph for CPU). See [Intermediate Representation and Operation Sets](../_docs_MO_DG_IR_and_opsets.html) for details about supported operations.
+4. Allocates and fills memory for graph weights.
 
-@snippet src/template_executable_network.cpp executable_network:map_graph
+@snippet src/template_executable_network.cpp executable_network:compile_graph
 
-> **NOTE**: After all these steps, the backend specific graph is ready to create inference requests and perform inference.
+> **NOTE**: After all these steps, the hardware-specific graph is ready to create inference requests and perform inference.
 
 ### `ExecutableNetwork` Constructor Importing from Stream
 
-This constructor creates a backend specific graph by importing from a stream object:
+This constructor creates a hardware-specific graph by importing from a stream object:
 
-> **NOTE**: The export of backend specific graph is done in the `ExportImpl` method, and data formats must be the same for both import and export.
+> **NOTE**: The export of hardware-specific graph is done in the `ExportImpl` method, and data formats must be the same for both import and export.
 
 @snippet src/template_executable_network.cpp executable_network:ctor_import_stream
 
@@ -58,9 +57,9 @@ This constructor creates a backend specific graph by importing from a stream obj
 **Implementation details:**   
 Base InferenceEngine::ExecutableNetworkThreadSafeDefault class implements the public InferenceEngine::ExecutableNetworkThreadSafeDefault::Export method as following:
 - Writes `_plugin->GetName()` to the `model` stream.
-- Calls the `ExportImpl` method defined in a derived class to dump a backend specific graph.
+- Calls the `ExportImpl` method defined in a derived class to dump a hardware-specific graph.
 
-The implementation of the method should write all data to the `model` stream, which is required to import a backend specific graph later in the `Plugin::Import` method:
+The implementation of the method should write all data to the `model` stream, which is required to import a hardware-specific graph later in the `Plugin::Import` method:
 
 @snippet src/template_executable_network.cpp executable_network:export_impl
 
@@ -72,6 +71,7 @@ The method creates an asynchronous inference request and returns it. While the p
 - [Asynchronous inference request](@ref async_infer_request), which is a wrapper for a synchronous inference request and can run a pipeline asynchronously. Depending on a device pipeline structure, it can has one or several stages:
    - For single-stage pipelines, there is no need to define this method and create a class derived from InferenceEngine::AsyncInferRequestThreadSafeDefault. For single stage pipelines, a default implementation of this method creates InferenceEngine::AsyncInferRequestThreadSafeDefault wrapping a synchronous inference request and runs it asynchronously in the `_taskExecutor` executor.
    - For pipelines with multiple stages, such as performing some preprocessing on host, uploading input data to a device, running inference on a device, or downloading and postprocessing output data, schedule stages on several task executors to achieve better device use and performance. You can do it by creating a sufficient number of inference requests running in parallel. In this case, device stages of different inference requests are overlapped with preprocessing and postprocessing stage giving better performance.
+
    > **IMPORTANT**: It is up to you to decide how many task executors you need to optimally execute a device pipeline.
 
 @snippet src/template_executable_network.cpp executable_network:create_infer_request

docs/IE_PLUGIN_DG/InferRequest.md

@@ -1,7 +1,7 @@
 # Synchronous Inference Request {#infer_request}
 
 `InferRequest` class functionality:
-- Allocate input and output blobs needed for a backend-dependent network inference.
+- Allocate input and output blobs needed for a hardware-dependent network inference.
 - Define functions for inference process stages (for example, `preprocess`, `upload`, `infer`, `download`, `postprocess`). These functions can later be used to define an execution pipeline during [Asynchronous Inference Request](@ref async_infer_request) implementation.
 - Call inference stages one by one synchronously.
 
@@ -20,15 +20,9 @@ The example class has several fields:
 
 - `_executableNetwork` - reference to an executable network instance. From this reference, an inference request instance can take a task executor, use counter for a number of created inference requests, and so on.
 - `_profilingTask` - array of the `std::array<InferenceEngine::ProfilingTask, numOfStages>` type. Defines names for pipeline stages. Used to profile an inference pipeline execution with the Intel® instrumentation and tracing technology (ITT).
-- `_durations` - array of durations of each pipeline stage.
-- `_networkInputBlobs` - input blob map.
-- `_networkOutputBlobs` - output blob map.
-- `_parameters` - `ngraph::Function` parameter operations.
-- `_results` - `ngraph::Function` result operations.
-- backend specific fields:
-	- `_inputTensors` - inputs tensors which wrap `_networkInputBlobs` blobs. They are used as inputs to backend `_executable` computational graph.
-	- `_outputTensors` - output tensors which wrap `_networkOutputBlobs` blobs. They are used as outputs from backend `_executable` computational graph.
-	- `_executable` - an executable object / backend computational graph.
+- `_inputsNCHW` - input blob map
+- `_outputsNCHW` - output blob map
+- Several double values to hold an execution time for pipeline stages.
 
 ### `InferRequest` Constructor
 
@@ -36,6 +30,11 @@ The constructor initializes helper fields and calls methods which allocate blobs
 
 @snippet src/template_infer_request.cpp infer_request:ctor
 
+The implementation of function allocating device buffers is fully device-specific and not provided in the guide. 
+The implementation of function allocating host buffers assumes that the `Template` device works 
+natively only with the InferenceEngine::NCHW input and output layout, while the user can specify the InferenceEngine::NHWC as a layout 
+of InferenceEngine::CNNNetwork inputs and outputs and set InferenceEngine::NHWC blobs via the InferenceEngine::InferRequest::SetBlob method.
+
 > **NOTE**: Call InferenceEngine::CNNNetwork::getInputsInfo and InferenceEngine::CNNNetwork::getOutputsInfo to specify both layout and precision of blobs, which you can set with InferenceEngine::InferRequest::SetBlob and get with InferenceEngine::InferRequest::GetBlob. A plugin uses these hints to determine its internal layouts and precisions for input and output blobs if needed. 
 
 ### `~InferRequest` Destructor
@@ -52,27 +51,14 @@ Decrements a number of created inference requests:
 
 @snippet src/template_infer_request.cpp infer_request:infer_impl
 
-#### 1. `inferPreprocess`
-
 Below is the code of the the `inferPreprocess` method to demonstrate Inference Engine common preprocessing step handling:
 
 @snippet src/template_infer_request.cpp infer_request:infer_preprocess
 
 **Details:**
 * `InferImpl` must call the InferenceEngine::InferRequestInternal::execDataPreprocessing function, which executes common Inference Engine preprocessing step (for example, applies resize or color conversion operations) if it is set by the user. The output dimensions, layout and precision matches the input information set via InferenceEngine::CNNNetwork::getInputsInfo.
-* If `inputBlob` passed by user differs in terms of precisions from precision expected by plugin, `blobCopy` is performed which does actual precision conversion.
-
-#### 2. `startPipeline`
-
-Executes a pipeline synchronously using `_executable` object:
-
-@snippet src/template_infer_request.cpp infer_request:start_pipeline
-
-#### 3. `inferPostprocess`
-
-Converts output blobs if precisions of backend output blobs and blobs passed by user are different:
-
-@snippet src/template_infer_request.cpp infer_request:infer_postprocess
+* To handle both InferenceEngine::NCHW and InferenceEngine::NHWC input layouts, the `TemplateInferRequest` class has the `_inputsNCHW` field, which holds blobs in the InferenceEngine::NCHW layout. During Inference Request execution, `InferImpl` copies from the input InferenceEngine::NHWC layout to `_inputsNCHW` if needed.
+* The next logic of `InferImpl` works with `_inputsNCHW`.
 
 ### `GetPerformanceCounts()`
 

docs/IE_PLUGIN_DG/Intro.md

@@ -12,36 +12,33 @@ Inference Engine plugin dynamic library consists of several main components:
 1. [Plugin class](@ref plugin):
 	- Provides information about devices of a specific type.
 	- Can create an [executable network](@ref executable_network) instance which represents a Neural 
-	Network backend specific graph structure for a particular device in opposite to the InferenceEngine::ICNNNetwork 
-	interface which is backend-independent.
+	Network hardware-specific graph structure for a particular device in opposite to the InferenceEngine::ICNNNetwork 
+	interface which is hardware-independent.
 	- Can import an already compiled graph structure from an input stream to an 
 	[executable network](@ref executable_network) object.
 2. [Executable Network class](@ref executable_network):
 	- Is an execution configuration compiled for a particular device and takes into account its capabilities.
 	- Holds a reference to a particular device and a task executor for this device.
 	- Can create several instances of [Inference Request](@ref infer_request).
-	- Can export an internal backend specific graph structure to an output stream.
-3. [Inference Request class](@ref infer_request):
+	- Can export an internal hardware-specific graph structure to an output stream.
+3. [Inference Request class](@ref infer_request) :
     - Runs an inference pipeline serially.
     - Can extract performance counters for an inference pipeline execution profiling.
-4. [Asynchronous Inference Request class](@ref async_infer_request):
+4. [Asynchronous Inference Request class](@ref async_infer_request) :
     - Wraps the [Inference Request](@ref infer_request) class and runs pipeline stages in parallel 
 	on several task executors based on a device-specific pipeline structure.
 
 > **NOTE**: This documentation is written based on the `Template` plugin, which demonstrates plugin 
 development details. Find the complete code of the `Template`, which is fully compilable and up-to-date,
-at `<dldt source dir>/docs/template_plugin`.
+at `<dldt source dir>/docs_developer/template_plugin`.
 
-Detailed guides
+Additional Resources
 -----------------------
 
 * [Build](@ref plugin_build) a plugin library using CMake\*
 * Plugin and its components [testing](@ref plugin_testing)
-* [Quantized networks](@ref quantized_networks)
-* [Writing ngraph transformations](@ref ngraph_transformation) guide
 
 API References
 -----------------------
 
 * [Inference Engine Plugin API](group__ie__dev__api.html)
-* [Inference Engine Transformation API](group__ie__transformation__api.html)

docs/IE_PLUGIN_DG/LowPrecisionModelRepresentation.md

@@ -1,18 +0,0 @@
-# Representation of low-precision models
-The goal of this document is to describe how optimized models are represented in OpenVINO Intermediate Representation (IR) and provide guidance on interpretation rules for such models at runtime. 
-Currently, there are two groups of optimization methods that can influence on the IR after applying them to the full-precision model:
-- **Sparsity**. It is represented by zeros inside the weights and this is up to the hardware plugin how to interpret these zeros (use weights as is or apply special compression algorithms and sparse arithmetic). No additional mask is provided with the model.
-- **Quantization**. The rest of this document is dedicated to the representation of quantized models.
-
-## Representation of quantized models
-The OpenVINO Toolkit represents all the quantized models using the so-called FakeQuantize operation (see the description in [this document](../MO_DG/prepare_model/convert_model/Legacy_IR_Layers_Catalog_Spec.md)). This operation is very expressive and allows mapping values from arbitrary input and output ranges. The whole idea behind that is quite simple: we project (discretize) the input values to the low-precision data type using affine transformation (with clamp and rounding) and then reproject discrete values back to the original range and data type. It can be considered as an emulation of the quantization process which happens at runtime.
-In order to be able to execute a particular DL operation in low-precision all its inputs should be quantized i.e. should have FakeQuantize between operation and data blobs.  The figure below shows an example of quantized Convolution which contains two FakeQuantize nodes: one for weights and one for activations (bias is quantized using the same parameters).
-![quantized_convolution]
-<div align="center">Figure 1. Example of quantized Convolution operation.</div>
-
-Starting from OpenVINO 2020.2 release all the quantized models are represented in the compressed form. It means that the weights of low-precision operations are converted into the target precision (e.g. INT8). It helps to substantially reduce the model size. The rest of the parameters can be represented in FLOAT32 or FLOAT16 precision depending on the input full-precision model used in the quantization process. Fig. 2 below shows an example of the part of the compressed IR.
-![quantized_model_example]
-<div align="center">Figure 2. Example of compressed quantized model.</div>  
-
-[quantized_convolution]: ../images/quantized_convolution.png
-[quantized_model_example]: ../images/quantized_model_example.png

docs/IE_PLUGIN_DG/Plugin.md

@@ -1,15 +1,5 @@
 # Plugin {#plugin}
 
-Inference Engine Plugin usually represents a wrapper around a backend. Backends can be:
-- OpenCL-like backend (e.g. clDNN library) for GPU devices.
-- MKLDNN backend for Intel CPU devices.
-- NVIDIA cuDNN for NVIDIA GPUs.
-
-The responsibility of Inference Engine Plugin:
-- Initializes a backend and throw exception in `Engine` constructor if backend cannot be initialized.
-- Provides information about devices enabled by a particular backend, e.g. how many devices, their properties and so on.
-- Loads or imports [executable network](@ref executable_network) objects.
-
 In addition to the Inference Engine Public API, the Inference Engine provides the Plugin API, which is a set of functions and helper classes that simplify new plugin development:
 
 - header files in the `inference_engine/src/plugin_api` directory
@@ -28,10 +18,8 @@ Based on that, declaration of a plugin class can look as follows:
 
 #### Class Fields
 
-The provided plugin class also has several fields:
+The provided plugin class also has a single field:
 
-* `_backend` - a backend engine that is used to perform actual computations for network inference. For `Template` plugin `ngraph::runtime::Backend` is used which performs computations using ngraph reference implementations.
-* `_waitExecutor` - a task executor that waits for a response from a device about device tasks completion.
 * `_cfg` of type `Configuration`:
 
 @snippet src/template_config.hpp configuration:header
@@ -40,7 +28,6 @@ As an example, a plugin configuration has three value parameters:
 
 - `deviceId` - particular device ID to work with. Applicable if a plugin supports more than one `Template` device. In this case, some plugin methods, like `SetConfig`, `QueryNetwork`, and `LoadNetwork`, must support the CONFIG_KEY(KEY_DEVICE_ID) parameter. 
 - `perfCounts` - boolean value to identify whether to collect performance counters during [Inference Request](@ref infer_request) execution.
-- `_streamsExecutorConfig` - configuration of `InferenceEngine::IStreamsExecutor` to handle settings of multi-threaded context.
 
 ### Engine Constructor
 
@@ -60,45 +47,25 @@ A plugin must define a device name enabled via the `_pluginName` field of a base
 of the public InferenceEngine::InferencePluginInternal::LoadNetwork method that calls plugin-specific `LoadExeNetworkImpl`, which is defined in a derived class.
 
 This is the most important function of the `Plugin` class and creates an instance of compiled `ExecutableNetwork`,
-which holds a backend-dependent compiled graph in an internal representation:
+which holds a hardware-dependent compiled graph in an internal representation:
 
 @snippet src/template_plugin.cpp plugin:load_exe_network_impl
 
 Before a creation of an `ExecutableNetwork` instance via a constructor, a plugin may check if a provided 
 InferenceEngine::ICNNNetwork object is supported by a device. In the example above, the plugin checks precision information.
 
-The very important part before creation of `ExecutableNetwork` instance is to call `TransformNetwork` method which applies ngraph transformation passes.
-
 Actual graph compilation is done in the `ExecutableNetwork` constructor. Refer to the [ExecutableNetwork Implementation Guide](@ref executable_network) for details.
 
 > **NOTE**: Actual configuration map used in `ExecutableNetwork` is constructed as a base plugin 
 > configuration set via `Plugin::SetConfig`, where some values are overwritten with `config` passed to `Plugin::LoadExeNetworkImpl`. 
 > Therefore, the config of  `Plugin::LoadExeNetworkImpl` has a higher priority.
 
-### `TransformNetwork()`
-
-The function accepts a const shared pointer to `ngraph::Function` object and performs the following steps:
-
-1. Deep copies a const object to a local object, which can later be modified.
-2. Applies common and plugin-specific transformations on a copied graph to make the graph more friendly to hardware operations. For details how to write custom plugin-specific transformation, please, refer to [Writing ngraph transformations](@ref ngraph_transformation) guide. See detailed topics about network representation:
-    * [Intermediate Representation and Operation Sets](../_docs_MO_DG_IR_and_opsets.html)
-    * [Quantized networks](@ref quantized_networks).
-
-@snippet src/template_plugin.cpp plugin:transform_network
-
-> **NOTE**: After all these transformations, a `ngraph::Function` object cointains operations which can be perfectly mapped to backend kernels. E.g. if backend has kernel computing `A + B` operations at once, the `TransformNetwork` function should contain a pass which fuses operations `A` and `B` into a single custom operation `A + B` which fits backend kernels set. 
-
 ### `QueryNetwork()`
 
 Use the method with the `HETERO` mode, which allows to distribute network execution between different 
 devices based on the `ngraph::Node::get_rt_info()` map, which can contain the `"affinity"` key.
 The `QueryNetwork` method analyzes operations of provided `network` and returns a list of supported
-operations via the InferenceEngine::QueryNetworkResult structure. The `QueryNetwork` firstly applies `TransformNetwork` passes to input `ngraph::Function` argument. After this, the transformed network in ideal case contains only operations are 1:1 mapped to kernels in computational backend. In this case, it's very easy to analyze which operations is supposed (`_backend` has a kernel for such operation or extensions for the operation is provided) and not supported (kernel is missed in `_backend`):
-
-1. Store original names of all operations in input `ngraph::Function`
-2. Apply `TransformNetwork` passes. Note, the names of operations in a transformed network can be different and we need to restore the mapping in the steps below.
-3. Construct `supported` and `unsupported` maps which contains names of original operations. Note, that since the inference is performed using ngraph reference backend, the decision whether the operation is supported or not depends on whether the latest OpenVINO opset contains such operation.
-4. `QueryNetworkResult.supportedLayersMap` contains only operations which are fully supported by `_backend`.
+operations via the InferenceEngine::QueryNetworkResult structure:
 
 @snippet src/template_plugin.cpp plugin:query_network
 
@@ -116,7 +83,7 @@ Sets new values for plugin configuration keys:
 @snippet src/template_plugin.cpp plugin:set_config
 
 In the snippet above, the `Configuration` class overrides previous configuration values with the new 
-ones. All these values are used during backend specific graph compilation and execution of inference requests.
+ones. All these values are used during hardware-specific graph compilation and execution of inference requests.
 
 > **NOTE**: The function must throw an exception if it receives an unsupported configuration key.
 
@@ -144,7 +111,7 @@ all devices of the same `Template` type with automatic logic of the `MULTI` devi
 in the `option` parameter as `{ CONFIG_KEY(KEY_DEVICE_ID), "deviceID" }`.
 - METRIC_KEY(SUPPORTED_METRICS) - list of metrics supported by a plugin
 - METRIC_KEY(SUPPORTED_CONFIG_KEYS) - list of configuration keys supported by a plugin that
-affects their behavior during a backend specific graph compilation or an inference requests execution
+affects their behavior during a hardware-specific graph compilation or an inference requests execution
 - METRIC_KEY(OPTIMIZATION_CAPABILITIES) - list of optimization capabilities of a device.
 For example, supported data types and special optimizations for them.
 - Any other device-specific metrics. In this case, place metrics declaration and possible values to 
@@ -161,9 +128,9 @@ The snippet below provides an example of the implementation for `GetMetric`:
 
 ### `ImportNetworkImpl()`
 
-The importing network mechanism allows to import a previously exported backend specific graph and wrap it 
+The importing network mechanism allows to import a previously exported hardware-specific graph and wrap it 
 using an [ExecutableNetwork](@ref executable_network) object. This functionality is useful if 
-backend specific graph compilation takes significant time and/or cannot be done on a target host 
+hardware-specific graph compilation takes significant time and/or cannot be done on a target host 
 device due to other reasons.
 
 **Implementation details:** The base plugin class InferenceEngine::InferencePluginInternal implements InferenceEngine::InferencePluginInternal::ImportNetwork 
@@ -174,7 +141,7 @@ implementation and define an output blob structure up to its needs. This
 can be useful if a plugin exports a blob in a special format for integration with other frameworks 
 where a common Inference Engine header from a base class implementation is not appropriate. 
 
-During export of backend specific graph using `ExecutableNetwork::Export`, a plugin may export any 
+During export of hardware-specific graph using `ExecutableNetwork::Export`, a plugin may export any 
 type of information it needs to import a compiled graph properly and check its correctness. 
 For example, the export information may include:
 
@@ -183,7 +150,7 @@ For example, the export information may include:
 throw an exception if the `model` stream contains wrong data. For example, if devices have different 
 capabilities and a graph compiled for a particular device cannot be used for another, such type of 
 information must be stored and checked during the import. 
-- Compiled backend specific graph itself
+- Compiled hardware-specific graph itself
 - Information about precisions and shapes set by the user
 
 @snippet src/template_plugin.cpp plugin:import_network_impl
@@ -191,7 +158,7 @@ information must be stored and checked during the import.
 Create Instance of Plugin Class
 ------------------------
 
-Inference Engine plugin library must export only one function creating a plugin instance using IE_DEFINE_PLUGIN_CREATE_FUNCTION macro:
+Inference Engine plugin library must export only one function creating a plugin instance:
 
 @snippet src/template_plugin.cpp plugin:create_plugin_engine
 

docs/IE_PLUGIN_DG/PluginTesting.md

@@ -1,58 +1,40 @@
 # Plugin Testing {#plugin_testing}
 
-Inference Engine (IE) tests infrastructure provides a predefined set of functional tests and utilities. They are used to verify a plugin using the Inference Engine public API.
+Inference Engine (IE) tests infrastructure provides a predefined set of functional tests and utilities exported via the Inference
+Engine developer package. They are used to verify a plugin using the Inference Engine public API.
 All the tests are written in the [Google Test C++ framework](https://github.com/google/googletest).
 
-Inference Engine Plugin tests are included in the `IE::funcSharedTests` CMake target which is built within the OpenVINO repository
-(see [Build Plugin Using CMake](@ref plugin_build) guide). This library contains tests definitions (the tests bodies) which can be parametrized and instantiated in plugins depending on whether a plugin supports a particular feature, specific sets of parameters for test on supported operation set and so on.
-
-Test definitions are splitted into tests class declaration (see `inference_engine/tests/functional/plugin/shared/include`) and tests class implementation (see `inference_engine/tests/functional/plugin/shared/src`) and include the following scopes of plugin conformance tests:
-
-1. **Behavior tests** (`behavior` sub-folder), which are a separate test group to check that a plugin satisfies basic Inference
-Engine concepts: plugin creation, multiple executable networks support, multiple synchronous and asynchronous inference requests support, and so on. See the next section with details how to instantiate the tests definition class with plugin-specific parameters.
-
-2. **Single layer tests** (`single_layer_tests` sub-folder). This groups of tests checks that a particular single layer can be inferenced on a device. An example of test instantiation based on test definition from `IE::funcSharedTests` library:
-
-    - From the declaration of convolution test class we can see that it's a parametrized GoogleTest based class with the `convLayerTestParamsSet` tuple of parameters:
-
-    @snippet single_layer_tests/convolution.hpp test_convolution:definition
-
-    - Based on that, define a set of parameters for `Template` plugin functional test instantiation:
-
-    @snippet single_layer_tests/convolution.cpp test_convolution:declare_parameters
-
-    - Instantiate the test itself using standard GoogleTest macro `INSTANTIATE_TEST_CASE_P`:
-
-    @snippet single_layer_tests/convolution.cpp test_convolution:instantiate
-
-3. **Sub-graph tests** (`subgraph_tests` sub-folder). This group of tests is designed to tests small patterns or combination of layers. E.g. when a particular topology is being enabled in a plugin e.g. TF ResNet-50, there is no need to add the whole topology to test tests. In opposite way, a particular repetative subgraph or pattern can be extracted from `ResNet-50` and added to the tests. The instantiation of the sub-graph tests is done in the same way as for single layer tests.  
-> **Note**, such sub-graphs or patterns for sub-graph tests should be added to `IE::ngraphFunctions` library first (this library is a pre-defined set of small `ngraph::Function`) and re-used in sub-graph tests after.
-
-4. **HETERO tests** (`subgraph_tests` sub-folder) contains tests for `HETERO` scenario (manual or automatic affinities settings, tests for `QueryNetwork`).
-
-5. **Other tests**, which contain tests for other scenarios and has the following types of tests:
-    - Tests for execution graph
-    - Etc.
-
-To use these tests for your own plugin development, link the `IE::funcSharedTests` library to your test binary and instantiate required test cases with desired parameters values.
-
-> **NOTE**: A plugin may contain its own tests for use cases that are specific to hardware or need to be extensively tested.
-
 To build test binaries together with other build artifacts, use the `make all` command. For details, see
 [Build Plugin Using CMake*](@ref plugin_build).
 
-### Tests for plugin-specific ngraph transformations
+Inference Engine Plugin tests are included in the `funcSharedTests` CMake target which is built within the  Deep Learning Deployment Toolkit (DLDT) repository
+(see [Build Plugin Using CMake](@ref plugin_build) guide).
 
-Please, refer to [Transformation testing](@ref ngraph_transformation) guide.
+Test definitions:
 
-### How to Extend Inference Engine Plugin Tests
+1. **Conformance tests**, which are a separate test group to check that a plugin satisfies basic Inference
+Engine concepts: plugin creation, multiple executable networks support, multiple synchronous and asynchronous inference requests support, and so on.
+2. **Other API tests**, which contain the following types of tests:
+    - Per-layer tests. Located in the `single_layer_tests`and `subgraph_tests` folders.
+    - Tests for integration with the `InferenceEngine::Core` class. Located in the the `ie_class` folder.
+    - Tests to check that IE common preprocessing works with your plugin. The `io_blob_tests` folder.
+
+To use these tests for your own plugin development, link the `funcSharedTests` library to your test binary and
+instantiate required test cases with desired parameters values.
+
+> **NOTE**: A plugin may contain its own tests for use cases that are specific to hardware or need to be extensively
+> tested. Depending on your device positioning, you can implement more specific tests for your device. Such tests can
+> be defined both for conformance and other API tests groups within your own test binary.
+
+How to Extend Inference Engine Plugin Tests
+========================
 
 Inference Engine Plugin tests are open for contribution.
-Add common test case definitions applicable for all plugins to the `IE::funcSharedTests` target within the DLDT repository. Then, any other plugin supporting corresponding functionality can instantiate the new test.
+Add common test case definitions applicable for all plugins to the `funcSharedTests` target within the DLDT repository. Then, any other plugin supporting corresponding functionality can instantiate the new test.
 
 All Inference Engine per-layer tests check test layers functionality. They are developed using nGraph functions
 as input graphs used by tests. In this case, to test a new layer with layer tests, extend
-the `IE::ngraphFunctions` library, which is also included in the Inference Engine Developer package, with a new nGraph function
+the `ngraphFunctions` CMake target, which is also included in the Inference Engine Developer package, with a new nGraph function
 including the corresponding operation.
 
-> **NOTE**: When implementing a new subgraph test, add new single-layer tests for each operation of the subgraph if such test does not exist.
+> **NOTE**: When implementing a new subgraph test, add new single-layer tests for each operation of the subgraph.

docs/IE_PLUGIN_DG/QuantizedNetworks.md

@@ -1,49 +0,0 @@
-# Quantized networks compute and restrictions {#quantized_networks}
-
-One of the feature of Inference Engine is the support of quantized networks with different precisions: INT8, INT4, etc.
-However, it is up to the plugin to define what exact precisions are supported by the particular HW.
-All quantized networks which can be expressed in IR have a unified representation by means of *FakeQuantize* operation. 
-For more details about low-precision model representation please refer to this [document](LowPrecisionModelRepresentation.md).
-
-### Interpreting FakeQuantize at runtime
-During the model load each plugin can interpret quantization rules expressed in *FakeQuantize* operations:
-- Independently based on the definition of *FakeQuantize* operation.
-- Using a special library of low-precision transformations (LPT) which applies common rules for generic operations,
-such as Convolution, Fully-Connected, Eltwise, etc., and translates "fake-quantized" models into the models with low-precision operations. For more information about low-precision flow please refer to the following [document](../IE_DG/Int8Inference.md). 
-
-Here we provide only a high-level overview of the interpretation rules of FakeQuantize. 
-At runtime each FakeQuantize can be split into two independent operations: **Quantize** and **Dequantize**. 
-The former one is aimed to transform the input data into the target precision while the latter transforms the resulting values back to the original range and precision. 
-In practice *Dequantize* operations can be propagated forward through the linear operations, such as *Convolution* or *Fully-Connected*, 
-and in some cases fused with the following *Quantize* operation for the next layer into the so-called *Requantize* operation (see Fig. 1).
-
-![qdq_propagation]
-<div align="center">Figure 1. Quantization operations propagation at runtime. Q, DQ, RQ stand for Quantize, Dequantize, and Requantize correspondingly.</div>
-
-From the calculation standpoint, the FakeQuantize formula also is split into two parts accordingly:  
-`output = round((x - input_low) / (input_high - input_low) * (levels-1)) / (levels-1) * (output_high - output_low) + output_low`  
-The first part of this formula represents *Quantize* operation:  
-`q = round((x - input_low) / (input_high - input_low) * (levels-1))`  
-The second is responsible for the dequantization:  
-`r = q / (levels-1) * (output_high - output_low) + output_low`  
-From the scale/zero-point notation standpoint the latter formula can be written as follows:  
-`r = (output_high - output_low) / (levels-1) * (q + output_low / (output_high - output_low) * (levels-1))`  
-Thus we can define:
-- **Scale** as `(output_high - output_low) / (levels-1)`
-- **Zero-point** as `-output_low / (output_high - output_low) * (levels-1)`
-
-**Note**: During the quantization process the values `input_low`, `input_high`, `output_low`, `output_high` are selected so that to map a floating-point zero exactly to an integer value (zero-point) and vice versa.
-
-## Quantization specifics and restrictions
-In general, OpenVINO can represent and execute quantized models from different sources. However, the Post-training Optimization Toolkit (POT)
-is considered the default way to get optimized models. Since the POT supports HW-aware quantization it means that specific rules can be implemented in it for 
-the particular HW. However, it is reasonable to have compatibility with general-purpose HW such as CPU and GPU and support their quantization schemes.
-Below we define these rules as follows:
-- Support of mixed-precision models where some layers can be kept in the floating-point precision.
-- Per-channel quantization of weights of Convolutional and Fully-Connected layers.
-- Per-channel quantization of activations for channel-wise and element-wise operations, e.g. Depthwise Convolution, Eltwise Add/Mul, ScaleShift.
-- Symmetric and asymmetric quantization of weights and activations with the support of per-channel scales and zero-points.
-- Non-unified quantization parameters for Eltwise and Concat operations.  
-- Non-quantized network output, i.e. there are no quantization parameters for it.
-
-[qdq_propagation]: ../images/qdq_propagation.png

docs/IE_PLUGIN_DG/images/qdq_propagation.png

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:e0bab657bf979494cb84459e29024e5b8b9cd320388c62c6a91b74b897b19718
-size 18108

docs/IE_PLUGIN_DG/images/quantized_convolution.png

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:71365e85be040eb01ed524e568b332d9bb6222c760686c54db4e754f587082c2
-size 31032

docs/IE_PLUGIN_DG/images/quantized_model_example.png

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:3f68e826cfac63d8e6f8d77aa5b7fc61957a872dfb09b38695fb481044a6ddd5
-size 48327

docs/IE_PLUGIN_DG/layout.xml

@@ -10,10 +10,9 @@
             </tab>
         </tab>
         <!-- Additional resources -->
-         <tab type="usergroup" visibile="no" title="DETAILED GUIDES">
+         <tab type="usergroup" visibile="no" title="ADDITIONAL RESOURCES">
             <tab type="user" url="@ref plugin_build" visibile="yes" title="Build Your Plugin with CMake*"/>
             <tab type="user" url="@ref plugin_testing" visibile="yes" title="Test Your Plugin"/>
-            <tab type="user" url="@ref quantized_networks" visibile="yes" title="Quantized networks guide"/>
         </tab>
         <!-- API References -->
         <tab type="usergroup" title="API REFERENCE">
@@ -22,4 +21,4 @@
         </tab>
         <tab type="usergroup" title="MAIN OPENVINO™ DOCS" url="../index.html"/>
     </navindex>
-</doxygenlayout>
+</doxygenlayout>

docs/Inference_Engine_Development_Procedure/CONTRIBUTING.md

@@ -1,7 +1,7 @@
 # Inference Engine development configuration document {#openvino_docs_Inference_Engine_Development_Procedure_CONTRIBUTING}
 
 To create MakeFiles use following process or run build-after-clone.sh script located in the root
-folder if you use Ubuntu 18.04.
+folder if you use Ubuntu 16.04.
 To create Visual Studio project run create_vs_proj_x64.cmd from scripts folder. 
 
 ## Setting up the environment for development

docs/Inference_Engine_Development_Procedure/IE_Dev_Procedure.md

@@ -30,6 +30,7 @@
     * [IE TESTS]
     * [IE DOCS]
     * [IE MKLDNN]
+    * [IE FPGA]
     * [IE GNA]
     * [IE CLDNN]
     * [IE MYRIAD]

docs/Legal_Information.md

@@ -15,10 +15,3 @@ Your costs and results may vary.
 Intel technologies may require enabled hardware, software or service activation.
 
 © Intel Corporation.  Intel, the Intel logo, and other Intel marks are trademarks of Intel Corporation or its subsidiaries. \*Other names and brands may be claimed as the property of others.  
-
-## OpenVINO™ Logo
-To build equity around the project, the OpenVINO logo was created for both Intel and community usage. The logo may only be used to represent the OpenVINO toolkit and offerings built using the OpenVINO toolkit.
-
-## Logo Usage Guidelines
-The OpenVINO logo must be used in connection with truthful, non-misleading references to the OpenVINO toolkit, and for no other purpose.
-Modification of the logo or use of any separate element(s) of the logo alone is not allowed.

docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md

@@ -12,64 +12,34 @@ Model Optimizer produces an Intermediate Representation (IR) of the network, whi
 
 *  <code>.bin</code> - Contains the weights and biases binary data.
 
-> **TIP**: You also can work with the Model Optimizer inside the OpenVINO™ [Deep Learning Workbench](@ref workbench_docs_Workbench_DG_Introduction) (DL Workbench).
-> [DL Workbench](@ref workbench_docs_Workbench_DG_Introduction) is a platform built upon OpenVINO™ and provides a web-based graphical environment that enables you to optimize, fine-tune, analyze, visualize, and compare 
-> performance of deep learning models on various Intel® architecture
-> configurations. In the DL Workbench, you can use most of OpenVINO™ toolkit components.
-> <br>
-> Proceed to an [easy installation from Docker](@ref workbench_docs_Workbench_DG_Install_from_Docker_Hub) to get started.
-
 ## What's New in the Model Optimizer in this Release?
 
 * Common changes:
-    * Implemented several optimization transformations to replace sub-graphs of operations with HSwish, Mish, Swish and SoftPlus operations.
-    * Model Optimizer generates IR keeping shape-calculating sub-graphs **by default**. Previously, this behavior was triggered if the "--keep_shape_ops" command line parameter was provided. The key is ignored in this release and will be deleted in the next release. To trigger the legacy behavior to generate an IR for a fixed input shape (folding ShapeOf operations and shape-calculating sub-graphs to Constant), use the "--static_shape" command line parameter. Changing model input shape using the Inference Engine API in runtime may fail for such an IR.
-    * Fixed Model Optimizer conversion issues resulted in non-reshapeable IR using the Inference Engine reshape API.
-    * Enabled transformations to fix non-reshapeable patterns in the original networks:
-        * Hardcoded Reshape
-            * In Reshape(2D)->MatMul pattern
-            * Reshape->Transpose->Reshape when the pattern can be fused to the ShuffleChannels or DepthToSpace operation
-        * Hardcoded Interpolate
-            * In Interpolate->Concat pattern
-        * Added a dedicated requirements file for TensorFlow 2.X as well as the dedicated install prerequisites scripts.
-        * Replaced the SparseToDense operation with ScatterNDUpdate-4.
+    * Implemented generation of a compressed OpenVINO IR suitable for INT8 inference, which takes up to 4 times less disk space than an expanded one. Use the `--disable_weights_compression` Model Optimizer command-line parameter to get an expanded version.
+    * Implemented an optimization transformation to replace a sub-graph with the `Erf` operation into the `GeLU` operation.
+    * Implemented an optimization transformation to replace an upsamping pattern that is represented as a sequence of `Split` and `Concat` operations to a single `Interpolate` operation.
+    * Fixed a number of Model Optimizer bugs to generate reshape-able IRs of many models with the command line parameter `--keep_shape_ops`.
+    * Fixed a number of Model Optimizer transformations to set operations name in an IR equal to the original framework model operation name.
+    * The following operations are no longer generated with `version="opset1"`: `MVN`, `ROIPooling`, `ReorgYolo`. They became a part of new `opset2` operation set and generated with `version="opset2"`. Before this fix, the operations were generated with `version="opset1"` by mistake, they were not a part of `opset1` nGraph namespace; `opset1` specification was fixed accordingly.
+
 * ONNX*:
-    * Enabled an ability to specify the model output **tensor** name using the "--output" command line parameter.
-    * Added support for the following operations:
-        * Acosh
-        * Asinh
-        * Atanh
-        * DepthToSpace-11, 13
-        * DequantizeLinear-10 (zero_point must be constant)
-        * HardSigmoid-1,6
-        * QuantizeLinear-10 (zero_point must be constant)
-        * ReduceL1-11, 13
-        * ReduceL2-11, 13
-        * Resize-11, 13 (except mode="nearest" with 5D+ input, mode="tf_crop_and_resize", and attributes exclude_outside and extrapolation_value with non-zero values)
-        * ScatterND-11, 13
-        * SpaceToDepth-11, 13
+    * Added support for the following operations: `MeanVarianceNormalization` if normalization is performed over spatial dimensions.
+
 * TensorFlow*:
-    * Added support for the following operations:
-        * Acosh
-        * Asinh
-        * Atanh
-        * CTCLoss
-        * EuclideanNorm
-        * ExtractImagePatches
-        * FloorDiv
+    * Added support for the TensorFlow Object Detection models version 1.15.X.
+    * Added support for the following operations: `BatchToSpaceND`, `SpaceToBatchND`, `Floor`.
+
 * MXNet*:
     * Added support for the following operations:
-        * Acosh
-        * Asinh
-        * Atanh
-* Kaldi*:
-    * Fixed bug with ParallelComponent support. Now it is fully supported with no restrictions.
+        * `Reshape` with input shape values equal to -2, -3, and -4.
 
 > **NOTE:** 
 > [Intel® System Studio](https://software.intel.com/en-us/system-studio) is an all-in-one, cross-platform tool suite, purpose-built to simplify system bring-up and improve system and IoT device application performance on Intel® platforms. If you are using the Intel® Distribution of OpenVINO™ with Intel® System Studio, go to [Get Started with Intel® System Studio](https://software.intel.com/en-us/articles/get-started-with-openvino-and-intel-system-studio-2019).
 
 ## Table of Content
 
+* [Introduction to OpenVINO™ Deep Learning Deployment Toolkit](../IE_DG/Introduction.md)
+
 * [Preparing and Optimizing your Trained Model with Model Optimizer](prepare_model/Prepare_Trained_Model.md)
     * [Configuring Model Optimizer](prepare_model/Config_Model_Optimizer.md)
     * [Converting a Model to Intermediate Representation (IR)](prepare_model/convert_model/Converting_Model.md)
@@ -112,4 +82,4 @@ Model Optimizer produces an Intermediate Representation (IR) of the network, whi
 
 * [Known Issues](Known_Issues_Limitations.md)
 
-**Typical Next Step:** [Preparing and Optimizing your Trained Model with Model Optimizer](prepare_model/Prepare_Trained_Model.md)
+**Typical Next Step:** [Introduction to Intel® Deep Learning Deployment Toolkit](../IE_DG/Introduction.md)

docs/MO_DG/IR_and_opsets.md

@@ -240,10 +240,4 @@ So `opset1` and `opset2` share the same operation `ReLU`.
 
 To differentiate versions of the same operation type, like `ReLU`, the suffix `-N` is used in a versioned name of the operation.
 `N` usually refers to the first `opsetN` where this version of the operation is introduced.
-It is not guaranteed that new operations will be named according to that rule, the naming convention might be changed, but not for old operations which are frozen completely.
-
----
-## See Also
-
-* [Cut Off Parts of a Model](prepare_model/convert_model/Cutting_Model.md)
-
+It is not guaranteed that new operations will be named according to that rule, the naming convention might be changed, but not for old operations which are frozen completely.

docs/MO_DG/Known_Issues_Limitations.md

@@ -45,8 +45,3 @@ Possible workaround is to upgrade default protobuf compiler (libprotoc 2.5.0) to
 libprotoc 2.6.1.
 
 [protobuf_issue]: https://github.com/google/protobuf/issues/4272
-
----
-## See Also
-
-* [Known Issues and Limitations in the Inference Engine](../IE_DG/Known_Issues_Limitations.md)

docs/MO_DG/prepare_model/Config_Model_Optimizer.md

@@ -48,22 +48,14 @@ install_prerequisites_caffe.sh
 ```
 install_prerequisites_caffe.bat
 ```
-*   For TensorFlow\* 1.x on Linux:
+*   For TensorFlow\* on Linux:
 ```
 install_prerequisites_tf.sh
 ```
-*   For TensorFlow 1.x on Windows:
+*   For TensorFlow on Windows:
 ```
 install_prerequisites_tf.bat
 ```
-*   For TensorFlow\* 2.x on Linux:
-```
-install_prerequisites_tf2.sh
-```
-*   For TensorFlow 2.x on Windows:
-```
-install_prerequisites_tf2.bat
-```
 *   For MXNet\* on Linux:
 ```
 install_prerequisites_mxnet.sh
@@ -122,7 +114,7 @@ virtualenv -p /usr/bin/python3.6 .env3 --system-site-packages
 virtualenv -p /usr/bin/python3.6 .env3/bin/activate
 ```
 3.  Install all dependencies or only the dependencies for a specific framework:
-    *   To install dependencies for all frameworks except TensorFlow* 2.x:
+    *   To install dependencies for all frameworks:
 ```shell
 pip3 install -r requirements.txt
 ```
@@ -130,13 +122,9 @@ pip3 install -r requirements.txt
 ```shell
 pip3 install -r requirements_caffe.txt
 ```
-    *   To install dependencies only for TensorFlow 1.x:
+    *   To install dependencies only for TensorFlow:
 ```shell
 pip3 install -r requirements_tf.txt
-```
-    *   To install dependencies only for TensorFlow 2.x:
-```shell
-pip3 install -r requirements_tf2.txt
 ```
     *   To install dependencies only for MXNet:
 ```shell
@@ -260,14 +248,6 @@ python3 -m easy_install dist/protobuf-3.6.1-py3.6-win-amd64.egg
 set PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=cpp
 ```
 
----
 ## See Also
-docs\MO_DG\prepare_model\Config_Model_Optimizer.md
-docs\install_guides\installing-openvino-raspbian.md
 
 * [Converting a Model to Intermediate Representation (IR)](convert_model/Converting_Model.md)
-* [Install OpenVINO™ toolkit for Raspbian* OS](../../install_guides/installing-openvino-raspbian.md)
-* [Install Intel® Distribution of OpenVINO™ toolkit for Windows* 10](../../install_guides/installing-openvino-windows.md)
-* [Install Intel® Distribution of OpenVINO™ toolkit for Windows* with FPGA Support](../../install_guides/installing-openvino-windows-fpga.md)
-* [Install Intel® Distribution of OpenVINO™ toolkit for macOS*](../../install_guides/installing-openvino-macos.md)
-* [Configuration Guide for the Intel® Distribution of OpenVINO™ toolkit 2020.4 and the Intel® Vision Accelerator Design with an Intel® Arria® 10 FPGA SG2 (IEI's Mustang-F100-A10) on Linux* ](../../install_guides/VisionAcceleratorFPGA_Configure.md)

docs/MO_DG/prepare_model/Model_Optimizer_FAQ.md

@@ -365,7 +365,7 @@ Keep in mind that there is no space between and inside the brackets for input sh
 
 #### 58. What does the message "Please provide input layer names for input layer shapes" mean? <a name="question-58"></a>
 
-When specifying input shapes for several layers, you must provide names for inputs, whose shapes will be overwritten. For usage examples, see [Converting a Caffe* Model](convert_model/Convert_Model_From_Caffe.md. Additional information for `--input_shape` is in FAQ [#57](#question-57).
+When specifying input shapes for several layers, you must provide names for inputs, whose shapes will be overwritten. For usage examples, see [Converting a Caffe\* Model](https://docs.openvinotoolkit.org/latest/_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_Caffe.html). Additional information for `--input_shape` is in FAQ [#57](#question-57).
 
 #### 59. What does the message "Values cannot be parsed" mean? <a name="question-59"></a>
 
@@ -615,16 +615,3 @@ You need to specify values for each input of the model. For more information, re
 #### 102. What does the message "Operation _contrib_box_nms is not supported ..." mean? <a name="question-102"></a>
 
 It means that you trying to convert the topology which contains '_contrib_box_nms' operation which is not supported directly. However the sub-graph of operations including the '_contrib_box_nms' could be replaced with DetectionOutput layer if your topology is one of the gluoncv topologies. Specify '--enable_ssd_gluoncv' command line parameter for the Model Optimizer to enable this transformation.
-
-\htmlonly
-
-<script>
-  window.addEventListener('load', function(){
-    var questionID = getURLParameter('question'); /* this function is defined in openvino-layout.js */
-    if (questionID) {
-      window.location = window.location.pathname + '#' + encodeURI(questionID);
-    }
-  });
-</script>
-
-\endhtmlonly

docs/MO_DG/prepare_model/Supported_Frameworks_Layers.md

@@ -40,46 +40,6 @@ Standard MXNet\* symbols:
 
 | Symbol Name in MXNet\*| Limitations|
 | :----------| :----------|
-| _Plus | No |
-| _contrib_MultiBoxDetection | "force_suppress" = 1 is not supported, non-default variances are not supported |
-| _contrib_MultiBoxPrior | No |
-| _contrib_Proposal | No |
-| _copy | Not needed for inference |
-| _minus_scalar | No |
-| _mul_scalar | No |
-| _arange | No |
-| _contrib_AdaptiveAvgPooling2D | Converted to the Average Pooling with fixed paddings |
-| _maximum | No |
-| _minimum | No |
-| add_n | No |
-| arccosh | No |
-| arcsinh | No |
-| arctanh | No |
-| broadcast_add | No |
-| broadcast_mul | No |
-| cumsum | No |
-| div_scalar | No |
-| elementwise_sub | No |
-| elemwise_add | No |
-| elemwise_mul | No |
-| exp | No |
-| expand_dims | No |
-| greater_scalar | No |
-| minus_scalar | No |
-| null | Not needed for inference |
-| repeat | No |
-| rnn | No |
-| rnn_param_concat | No |
-| sigmoid | No |
-| slice | No |
-| slice_axis | No |
-| slice_channel | No |
-| slice_like | No |
-| stack | No |
-| swapaxis | No |
-| tile | No |
-| transpose | No |
-| zeros | No |
 | Activation | supported "act_type" = "relu", "sigmoid", "softrelu" or "tanh" |
 | BatchNorm | No |
 | Concat | No |
@@ -110,6 +70,43 @@ Standard MXNet\* symbols:
 | Tile | No |
 | UpSampling | No |
 | Where | No |
+| _Plus | No |
+| _contrib_MultiBoxDetection | "force_suppress" = 1 is not supported, non-default variances are not supported |
+| _contrib_MultiBoxPrior | No |
+| _contrib_Proposal | No |
+| _copy | Not needed for inference |
+| _minus_scalar | No |
+| _mul_scalar | No |
+| _arange | No |
+| _contrib_AdaptiveAvgPooling2D | Converted to the Average Pooling with fixed paddings |
+| _maximum | No |
+| _minimum | No |
+| add_n | No |
+| broadcast_add | No |
+| broadcast_mul | No |
+| cumsum | No |
+| div_scalar | No |
+| elementwise_sub | No |
+| elemwise_add | No |
+| elemwise_mul | No |
+| exp | No |
+| expand_dims | No |
+| greater_scalar | No |
+| minus_scalar | No |
+| null | Not needed for inference |
+| repeat | No |
+| rnn | No |
+| rnn_param_concat | No |
+| sigmoid | No |
+| slice | No |
+| slice_axis | No |
+| slice_channel | No |
+| slice_like | No |
+| stack | No |
+| swapaxis | No |
+| tile | No |
+| transpose | No |
+| zeros | No |
 
 
 ## TensorFlow\* Supported Operations
@@ -120,12 +117,9 @@ Standard TensorFlow\* operations:
 
 | Operation Name in TensorFlow\* | Limitations|
 | :----------| :----------|
-| Acosh | No |
 | Add | No |
 | AddN | No |
 | ArgMax | No |
-| Asinh | No |
-| Atanh | No |
 | AvgPool | No |
 | BatchToSpaceND | No |
 | BiasAdd | No |
@@ -150,10 +144,8 @@ Standard TensorFlow\* operations:
 | ExpandDims | No |
 | ExperimentalSparseWeightedSum | CPU only |
 | ExtractImagePatches | No |
-| EuclideanNorm | No |
 | Fill | No |
 | Floor | No |
-| FloorDiv | No |
 | FusedBatchNorm | No |
 | Gather | No |
 | GatherNd | Supported if it can be replaced with Gather |
@@ -293,14 +285,11 @@ Standard ONNX\* operators:
 | :----------| :----------|
 | Abs | No |
 | Acos | No |
-| Acosh | No |
 | Add | No |
 | Affine | No |
 | ArgMax | No |
 | Asin | No |
-| Asinh | No |
 | Atan | No |
-| Atanh | No |
 | AveragePool | No |
 | BatchMatMul | No |
 | BatchNormalization | No |
@@ -317,7 +306,7 @@ Standard ONNX\* operators:
 | Cosh | No |
 | Crop | No |
 | CumSum | No |
-| DequantizeLinear | No |
+| DequantizeLinear | Only in combination with QuantizeLinear, refer to the desc of the latter |
 | DetectionOutput (Intel experimental) | No |
 | Div | No |
 | Dropout | Not needed for inference |
@@ -362,13 +351,11 @@ Standard ONNX\* operators:
 | Pad | No |
 | Pow | No |
 | PriorBox (Intel experimental) | No |
-| QuantizeLinear | No |
+| QuantizeLinear | Only in combination with DequantizeLinear. When the ops following each other in the graph and the scale and zero-point values for these operations are the same (or explicitly shared), the combination is fused into a 'FakeQuantization'|
 | RNN | No |
 | ROIAlign | No |
 | Range | No |
 | Reciprocal | No |
-| ReduceL1 | No |
-| ReduceL2 | No |
 | ReduceMax | No |
 | ReduceMean | No |
 | ReduceMin | No |
@@ -376,10 +363,9 @@ Standard ONNX\* operators:
 | ReduceSum | No |
 | Relu | No |
 | Reshape | No |
-| Resize |  transformation mode `tf_crop_and_resize` is not supported, mode `nearest` is not supported for 5D+ inputs. |
+| Resize | Opset-10 version is supported |
 | ReverseSequence | No |
 | Scatter | Supported if fuse-able to ScatterUpdate. MYRIAD only |
-| ScatterND | No |
 | ScatterElements | Supported if fuse-able to ScatterUpdate. MYRIAD only |
 | Select | No |
 | Shape | No |

docs/MO_DG/prepare_model/convert_model/Convert_Model_From_Caffe.md

@@ -144,13 +144,3 @@ In this document, you learned:
 * Basic information about how the Model Optimizer works with Caffe\* models
 * Which Caffe\* models are supported
 * How to convert a trained Caffe\* model using the Model Optimizer with both framework-agnostic and Caffe-specific command-line options
-
----
-## See Also
-
-* [Converting a TensorFlow* Model](Convert_Model_From_TensorFlow.md)
-* [Converting an MXNet* Model](Convert_Model_From_MxNet.md)
-* [Converting a Kaldi* Model](Convert_Model_From_Kaldi.md)
-* [Converting an ONNX* Model](Convert_Model_From_ONNX.md)
-* [Converting a Model Using General Conversion Parameters](Converting_Model_General.md)
-* [Custom Layers in the Model Optimizer ](../customize_model_optimizer/Customize_Model_Optimizer.md)

docs/MO_DG/prepare_model/convert_model/Convert_Model_From_Kaldi.md

@@ -106,12 +106,3 @@ must be copied to `Parameter_0_for_Offset_fastlstm2.r_trunc__2Offset_fastlstm2.r
 
 ## Supported Kaldi\* Layers
 Refer to [Supported Framework Layers ](../Supported_Frameworks_Layers.md) for the list of supported standard layers.
-
----
-## See Also
-
-* [Converting a TensorFlow* Model](Convert_Model_From_TensorFlow.md)
-* [Converting an MXNet* Model](Convert_Model_From_MxNet.md)
-* [Converting a Caffe* Model](Convert_Model_From_Caffe.md)
-* [Converting an ONNX* Model](Convert_Model_From_ONNX.md)
-* [Custom Layers Guide](../../../HOWTO/Custom_Layers_Guide.md)

docs/MO_DG/prepare_model/convert_model/Convert_Model_From_MxNet.md

@@ -38,8 +38,7 @@ A summary of the steps for optimizing and deploying a model that was trained wit
 
 **Other supported topologies**
 
-* [GluonCV SSD and YOLO-v3 models](https://gluon-cv.mxnet.io/model_zoo/detection.html) can be converted using the following [instructions](mxnet_specific/Convert_GluonCV_Models.md).
-* [Style transfer model](https://github.com/zhaw/neural_style) can be converted using the following [instructions](mxnet_specific/Convert_Style_Transfer_From_MXNet.md).
+* Style transfer [model](https://github.com/zhaw/neural_style) can be converted using [instruction](mxnet_specific/Convert_Style_Transfer_From_MXNet.md),
 
 ## Convert an MXNet* Model <a name="ConvertMxNet"></a>
 
@@ -103,12 +102,3 @@ In this document, you learned:
 * Basic information about how the Model Optimizer works with MXNet\* models
 * Which MXNet\* models are supported
 * How to convert a trained MXNet\* model using the Model Optimizer with both framework-agnostic and MXNet-specific command-line options
-
----
-## See Also
-
-* [Converting a TensorFlow* Model](Convert_Model_From_TensorFlow.md)
-* [Converting a Caffe* Model](Convert_Model_From_Caffe.md)
-* [Converting a Kaldi* Model](Convert_Model_From_Kaldi.md)
-* [Converting an ONNX* Model](Convert_Model_From_ONNX.md)
-* [Custom Layers in the Model Optimizer](../customize_model_optimizer/Customize_Model_Optimizer.md)

docs/MO_DG/prepare_model/convert_model/Convert_Model_From_ONNX.md

@@ -78,12 +78,3 @@ There are no ONNX\* specific parameters, so only [framework-agnostic parameters]
 
 ## Supported ONNX\* Layers
 Refer to [Supported Framework Layers](../Supported_Frameworks_Layers.md) for the list of supported standard layers.
-
----
-## See Also
-
-* [Converting a TensorFlow* Model](Convert_Model_From_TensorFlow.md)
-* [Converting an MXNet* Model](Convert_Model_From_MxNet.md)
-* [Converting a Caffe* Model](Convert_Model_From_Caffe.md)
-* [Converting a Kaldi* Model](Convert_Model_From_Kaldi.md)
-* [Convert TensorFlow* BERT Model to the Intermediate Representation ](tf_specific/Convert_BERT_From_Tensorflow.md)

docs/MO_DG/prepare_model/convert_model/Convert_Model_From_TensorFlow.md

@@ -295,7 +295,6 @@ python3 mo_tf.py --input_model custom_model.pb --tensorflow_custom_layer_librari
 
 ## Convert TensorFlow* 2 Models <a name="Convert_From_TF2X"></a>
 
-In order to convert TensorFlow* 2 models, installation of dependencies from `requirements_tf2.txt` is required.
 TensorFlow* 2.X officially supports two model formats: SavedModel and Keras H5 (or HDF5).    
 Below are the instructions on how to convert each of them.
 
@@ -332,15 +331,6 @@ model = tf.keras.models.load_model('model.h5')
 tf.saved_model.save(model,'model')
 ```
 
-The Keras H5 model with a custom layer has specifics to be converted into SavedModel format.
-For example, the model with a custom layer `CustomLayer` from `custom_layer.py` is converted as follows:
-```python
-import tensorflow as tf
-from custom_layer import CustomLayer
-model = tf.keras.models.load_model('model.h5', custom_objects={'CustomLayer': CustomLayer})
-tf.saved_model.save(model,'model')
-```
-
 Then follow the above instructions for the SavedModel format. 
 
 > **NOTE:** Do not use other hacks to resave TensorFlow* 2 models into TensorFlow* 1 formats.    
@@ -375,12 +365,3 @@ In this document, you learned:
 * Which TensorFlow models are supported
 * How to freeze a TensorFlow model
 * How to convert a trained TensorFlow model using the Model Optimizer with both framework-agnostic and TensorFlow-specific command-line options
-
----
-## See Also
-
-* [Converting a Caffe* Model](Convert_Model_From_Caffe.md)
-* [Converting an MXNet* Model](Convert_Model_From_MxNet.md)
-* [Converting a Kaldi* Model](Convert_Model_From_Kaldi.md)
-* [Converting an ONNX* Model](Convert_Model_From_ONNX.md)
-* [Converting a Model Using General Conversion Parameters](Converting_Model_General.md)

docs/MO_DG/prepare_model/convert_model/Converting_Model_General.md

@@ -109,6 +109,7 @@ Framework-agnostic parameters:
   --disable_gfusing     Turn off fusing of grouped convolutions
   --enable_concat_optimization
                         Turn on Concat optimization.
+  --move_to_preprocess  Move mean values to IR preprocess section
   --extensions EXTENSIONS
                         Directory or a comma separated list of directories
                         with extensions. To disable all extensions including
@@ -126,10 +127,12 @@ Framework-agnostic parameters:
                         value, for example: "node_name->True". It will be
                         DEPRECATED in future releases. Use --input option to
                         specify a value for freezing.
-  --static_shape        Enables IR generation for fixed input shape (folding
-                        `ShapeOf` operations and shape-calculating sub-graphs
-                        to `Constant`). Changing model input shape using
-                        the Inference Engine API in runtime may fail for such an IR.
+  --generate_deprecated_IR_V7
+                        Force to generate old deprecated IR V7 with layers
+                        from old IR specification.
+  --keep_shape_ops      [ Experimental feature ] Enables `Shape` operation
+                        with all children keeping. This feature makes model
+                        reshapable in Inference Engine
   --disable_weights_compression
                         Disable compression and store weights with original
                         precision.
@@ -165,9 +168,12 @@ There are situations when the input data shape for the model is not fixed, like
 ## When to Reverse Input Channels <a name="when_to_reverse_input_channels"></a>
 Input data for your application can be of RGB or BRG color input order. For example, Inference Engine samples load input images in the BGR channels order. However, the model may be trained on images loaded with the opposite order (for example, most TensorFlow\* models are trained with images in RGB order). In this case, inference results using the Inference Engine samples may be incorrect. The solution is to provide `--reverse_input_channels` command line parameter. Taking this parameter, the Model Optimizer performs first convolution or other channel dependent operation weights modification so these operations output will be like the image is passed with RGB channels order.
 
-## When to Specify `--static_shape` Command Line Parameter
-If the `--static_shape` command line parameter is specified the Model Optimizer evaluates shapes of all operations in the model (shape propagation) for a fixed input(s) shape(s). During the shape propagation the Model Optimizer evaluates operations *Shape* and removes them from the computation graph. With that approach, the initial model which can consume inputs of different shapes may be converted to IR working with the input of one fixed shape only. For example, consider the case when some blob is reshaped from 4D of a shape *[N, C, H, W]* to a shape *[N, C, H \* W]*. During the model conversion the Model Optimize calculates output shape as a constant 1D blob with values *[N, C, H \* W]*. So if the input shape changes to some other value *[N,C,H1,W1]* (it is possible scenario for a fully convolutional model) then the reshape layer becomes invalid.
-Resulting Intermediate Representation will not be resizable with the help of Inference Engine.
+## When to Specify `--keep_shape_ops` Command Line Parameter
+The `--keep_shape_ops` is an **experimental** command line parameter, so the model conversion may fail if it is specified.
+
+By default, the Model Optimizer evaluates shapes of all operations in the model (shape propagation) for a fixed input(s) shape(s). During the shape propagation the Model Optimizer evaluates operations *Shape* and removes them from the computation graph. With that approach, the initial model which can consume inputs of different shapes may be converted to IR working with the input of one fixed shape only. For example, consider the case when some blob is reshaped from 4D of a shape *[N, C, H, W]* to a shape *[N, C, H \* W]*. During the model conversion the Model Optimize calculates output shape as a constant 1D blob with values *[N, C, H \* W]*. So if the input shape changes to some other value *[N,C,H1,W1]* (it is possible scenario for a fully convolutional model) then the reshape layer becomes invalid.
+
+If the `--keep_shape_ops` command line parameter is specified then the Model Optimizer keeps *Shape* operations in the model and inserts additional layers to convert the graph layout from NHWC to NCHW layout if necessary.
 
 ## Examples of CLI Commands
 
@@ -233,13 +239,3 @@ Otherwise, it will be casted to data type passed to `--data_type` parameter (by
 ```sh
 python3 mo.py --input_model FaceNet.pb --input "placeholder_layer_name->[0.1 1.2 2.3]"
 ```
-
----
-## See Also
-
-* [Converting a Cafee* Model](Convert_Model_From_Caffe.md)
-* [Converting a TensorFlow* Model](Convert_Model_From_TensorFlow.md)
-* [Converting an MXNet* Model](Convert_Model_From_MxNet.md)
-* [Converting an ONNX* Model](Convert_Model_From_ONNX.md)
-* [Converting a Kaldi* Model](Convert_Model_From_Kaldi.md)
-* [Using Shape Inference](../../../IE_DG/ShapeInference.md)

docs/MO_DG/prepare_model/convert_model/Cutting_Model.md

@@ -389,11 +389,4 @@ In this case, when `--input_shape` is specified and the node contains multiple i
 The correct command line is:
 ```sh
 python3 mo.py --input_model=inception_v1.pb --input=0:InceptionV1/InceptionV1/Conv2d_1a_7x7/convolution --input_shape=[1,224,224,3]
-```
-
----
-## See Also
-
-* [Sub-Graph Replacement in the Model Optimizer](../customize_model_optimizer/Subgraph_Replacement_Model_Optimizer.md)
-* [Extending the Model Optimizer with New Primitives](../customize_model_optimizer/Extending_Model_Optimizer_with_New_Primitives.md)
-* [Converting a Model Using General Conversion Parameters](Converting_Model_General.md)
+```

docs/MO_DG/prepare_model/convert_model/IR_suitable_for_INT8_inference.md

@@ -34,11 +34,4 @@ Weights compression leaves `FakeQuantize` output arithmetically the same and wei
 See the visualization of `Convolution` with the compressed weights:
 ![](../../img/compressed_int8_Convolution_weights.png)
 
-Both Model Optimizer and Post-Training Optimization tool generate a compressed IR by default. To generate an expanded INT8 IR, use `--disable_weights_compression`.
-
----
-## See Also
-
-* [Quantization](@ref pot_compression_algorithms_quantization_README)
-* [Optimization Guide](../../../optimization_guide/dldt_optimization_guide.md)
-* [Low Precision Optimization Guide](@ref pot_docs_LowPrecisionOptimizationGuide)
+Both Model Optimizer and Post-Training Optimization tool generate a compressed IR by default. To generate an expanded INT8 IR, use `--disable_weights_compression`.

docs/MO_DG/prepare_model/convert_model/kaldi_specific/Aspire_Tdnn_Model.md

@@ -110,8 +110,3 @@ speech_sample -i feats.ark,ivector_online_ie.ark -m final.xml -d CPU -o predicti
 
 Results can be decoded as described in "Use of Sample in Kaldi* Speech Recognition Pipeline" chapter 
 in [the Speech Recognition Sample description](../../../../../inference-engine/samples/speech_sample/README.md).
-
----
-## See Also
-
-* [Converting a Kaldi Model](../Convert_Model_From_Kaldi.md)

docs/MO_DG/prepare_model/convert_model/mxnet_specific/Convert_GluonCV_Models.md

@@ -1,26 +0,0 @@
-# Converting GluonCV* Models {#openvino_docs_MO_DG_prepare_model_convert_model_mxnet_specific_Convert_GluonCV_Models}
-
-This document provides the instructions and examples on how to use Model Optimizer to convert [GluonCV SSD and YOLO-v3 models](https://gluon-cv.mxnet.io/model_zoo/detection.html) to IR.
-
-1. Choose the topology available from the [GluonCV Moodel Zoo](https://gluon-cv.mxnet.io/model_zoo/detection.html) and export to the MXNet format using the GluonCV API. For example, for the `ssd_512_mobilenet1.0` topology: 
-```python
-from gluoncv import model_zoo, data, utils
-from gluoncv.utils import export_block
-net = model_zoo.get_model('ssd_512_mobilenet1.0_voc', pretrained=True)
-export_block('ssd_512_mobilenet1.0_voc', net, preprocess=True, layout='HWC')
-```
-As a result, you will get an MXNet model representation in `ssd_512_mobilenet1.0.params` and `ssd_512_mobilenet1.0.json` files generated in the current directory. 
-2. Run the Model Optimizer tool specifying the `--enable_ssd_gluoncv` option. Make sure the `--input_shape` parameter is set to the input shape layout of your model (NHWC or NCHW). The examples below illustrates running the Model Optimizer for the SSD and YOLO-v3 models trained with the NHWC layout and located in the `<model_directory>`:
-* **For GluonCV SSD topologies:**
-```sh
-python3 mo_mxnet.py --input_model <model_directory>/ssd_512_mobilenet1.0.params --enable_ssd_gluoncv --input_shape [1,512,512,3] --input data
-```
-* **For YOLO-v3 topology:**
-   * To convert the model:
-   ```sh
-   python3 mo_mxnet.py --input_model <model_directory>/yolo3_mobilenet1.0_voc-0000.params  --input_shape [1,255,255,3]
-   ```
-   * To convert the model with replacing the subgraph with RegionYolo layers:
-   ```sh
-   python3 mo_mxnet.py --input_model <model_directory>/models/yolo3_mobilenet1.0_voc-0000.params  --input_shape [1,255,255,3] --transformations_config "mo/extensions/front/mxnet/yolo_v3_mobilenet1_voc.json"
-   ```

docs/MO_DG/prepare_model/convert_model/tf_specific/Convert_BERT_From_Tensorflow.md

@@ -114,6 +114,7 @@ python3 ./mo_tf.py
 --input_model inference_graph.pb 
 --input "IteratorGetNext:0{i32}[1 128],IteratorGetNext:1{i32}[1 128],IteratorGetNext:4{i32}[1 128]" 
 --disable_nhwc_to_nchw 
+--keep_shape_ops
 ```
 For other applicable parameters, refer to [Convert Model from TensorFlow](../Convert_Model_From_TensorFlow.md).
 

docs/MO_DG/prepare_model/convert_model/tf_specific/Convert_Object_Detection_API_Models.md

@@ -1,7 +1,9 @@
 # Converting TensorFlow* Object Detection API Models {#openvino_docs_MO_DG_prepare_model_convert_model_tf_specific_Convert_Object_Detection_API_Models}
 
 > **NOTES**:
-> * Starting with the 2021.1 release, the Model Optimizer converts the TensorFlow\* Object Detection API SSDs, Faster and Mask RCNNs topologies keeping shape-calculating sub-graphs by default, so topologies can be re-shaped in the Inference Engine using dedicated reshape API. Refer to [Using Shape Inference](../../../../IE_DG/ShapeInference.md) for more information on how to use this feature. It is possible to change the both spatial dimensions of the input image and batch size.
+>
+> * Starting with the 2019 R1 release, the Model Optimizer supports the `--keep_shape_ops` command line parameter that allows you to convert the TensorFlow\* Object Detection API Faster and Mask RCNNs topologies so they can be re-shaped in the Inference Engine using dedicated reshape API. Refer to [Using Shape Inference](../../../../IE_DG/ShapeInference.md) for more information on how to use this feature. It is possible to change the both spatial dimensions of the input image and batch size.
+> * Starting with the 2018 R4 release, the Model Optimizer supports the `--input_shape` command line parameter for the TensorFlow\* Object Detection API topologies. Refer to the [Custom Input Shape](#tf_od_custom_input_shape) for more information.
 > * To generate IRs for SSD topologies, the Model Optimizer creates a number of `PriorBoxClustered` layers instead of a constant node with prior boxes calculated for the particular input image size. This change allows you to reshape the topology in the Inference Engine using dedicated Inference Engine API. The reshaping is supported for all SSD topologies except FPNs which contain hardcoded shapes for some operations preventing from changing topology input shape.  
 
 ## How to Convert a Model

docs/benchmarks/performance_benchmarks.md

@@ -2,97 +2,117 @@
 
 ## Increase Performance for Deep Learning Inference
 
-The [Intel® Distribution of OpenVINO™ toolkit](https://software.intel.com/en-us/openvino-toolkit) helps accelerate deep learning inference across a variety of Intel® processors and accelerators. Rather than a one-size-fits-all solution, Intel offers a powerful portfolio of scalable hardware and software solutions, powered by the Intel® Distribution of OpenVINO™ toolkit, to meet the various performance, power, and price requirements of any use case. The benchmarks below demonstrate high performance gains on several public neural networks for a streamlined, quick deployment on **Intel® CPU and VPU** platforms. Use this data to help you decide which hardware is best for your applications and solutions, or to plan your AI workload on the Intel computing already included in your solutions.
+The [Intel® Distribution of OpenVINO™ toolkit](https://software.intel.com/en-us/openvino-toolkit) helps accelerate deep learning inference across a variety of Intel® processors and accelerators. Rather than a one-size-fits-all solution, Intel offers a powerful portfolio of scalable hardware and software solutions, powered by the Intel® Distribution of OpenVINO™ toolkit, to meet the various performance, power, and price requirements of any use case. The benchmarks below demonstrate high performance gains on several public neural networks for a streamlined, quick deployment on **Intel® CPU, VPU and FPGA** platforms. Use this data to help you decide which hardware is best for your applications and solutions, or to plan your AI workload on the Intel computing already included in your solutions.
 
 Measuring inference performance involves many variables and is extremely use-case and application dependent. We use the below four parameters for measurements, which are key elements to consider for a successful deep learning inference application:
 
-1. **Throughput** - Measures the number of inferences delivered within a latency threshold. (for example, number of Frames Per Second - FPS). When deploying a system with deep learning inference, select the throughput that delivers the best trade-off between latency and power for the price and performance that meets your requirements.
+1. **Throughput** - Measures the number of inferences delivered within a latency threshold. (for example, number of frames per second). When deploying a system with deep learning inference, select the throughput that delivers the best trade-off between latency and power for the price and performance that meets your requirements.
 2. **Value** - While throughput is important, what is more critical in edge AI deployments is the performance efficiency or performance-per-cost. Application performance in throughput per dollar of system cost is the best measure of value.
 3. **Efficiency** - System power is a key consideration from the edge to the data center. When selecting deep learning solutions, power efficiency (throughput/watt) is a critical factor to consider. Intel designs provide excellent power efficiency for running deep learning workloads.
-4. **Latency** - This measures the synchronous execution of inference requests and is reported in milliseconds. Each inference request (for example: preprocess, infer, postprocess) is allowed to complete before the next is started. This performance metric is relevant in usage scenarios where a single image input needs to be acted upon as soon as possible. An example would be the healthcare sector where medical personnel only request analysis of a single ultra sound scanning image or in real-time or near real-time applications for example an industrial robot's response to actions in its environment or obstacle avoidance for autonomous vehicles.   
+4. **Total Benefit** (Most applicable for Intel® VPU Platforms) - Combining the factors of value and efficiency can be a good way to compare which hardware yields the best performance per watt and per dollar for your particular use case. 
 
-\htmlonly
-<!-- these CDN links and scripts are required.  Add them to the <head> of your website -->
-<link href="https://fonts.googleapis.com/css2?family=Roboto:wght@100;300;400;500;600;700;900&display=swap" rel="stylesheet" type="text/css">
-<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/font-awesome/4.7.0/css/font-awesome.min.css" type="text/css">
-<script src="https://cdn.jsdelivr.net/npm/chart.js@2.9.3/dist/Chart.min.js"></script>
-<script src="https://cdn.jsdelivr.net/npm/chartjs-plugin-datalabels"></script>
-<script src="https://cdnjs.cloudflare.com/ajax/libs/chartjs-plugin-annotation/0.5.7/chartjs-plugin-annotation.min.js"></script> 
-<script src="https://cdn.jsdelivr.net/npm/chartjs-plugin-barchart-background@1.3.0/build/Plugin.Barchart.Background.min.js"></script>
-<script src="https://cdn.jsdelivr.net/npm/chartjs-plugin-deferred@1"></script>
-<!-- download this file and place on your server (or include the styles inline) -->
-<link rel="stylesheet" href="ovgraphs.css" type="text/css">
-\endhtmlonly
+---
 
+## Intel® Xeon® E-2124G<a name="xeon-e"></a>
 
-\htmlonly
-<script src="bert-large-uncased-whole-word-masking-squad-int8-0001-ov-2021-1-096.js" id="bert-large-uncased-whole-word-masking-squad-int8-0001-ov-2021-1-096"></script>
-\endhtmlonly
+![](img/throughput_xeon_e212g.png)
+![](img/value_xeon_e212g.png)
+![](img/eff_xeon_e212g.png)
 
-\htmlonly
-<script src="deeplabv3-tf-ov-2021-1-096.js" id="deeplabv3-tf-ov-2021-1-096"></script>
-\endhtmlonly
+---
 
-\htmlonly
-<script src="densenet-121-tf-ov-2021-1-096.js" id="densenet-121-tf-ov-2021-1-096"></script>
-\endhtmlonly
+## Intel® Xeon® Silver 4216R <a name="xeon-silver"></a>
 
-\htmlonly
-<script src="faster-rcnn-resnet50-coco-tf-ov-2021-1-096.js" id="faster-rcnn-resnet50-coco-tf-ov-2021-1-096"></script>
-\endhtmlonly
+![](img/throughput_xeon_silver.png)
+![](img/value_xeon_silver.png)
+![](img/eff_xeon_silver.png)
 
-\htmlonly
-<script src="googlenet-v1-tf-ov-2021-1-096.js" id="googlenet-v1-tf-ov-2021-1-096"></script>
-\endhtmlonly
+---
 
-\htmlonly
-<script src="inception-v3-tf-ov-2021-1-096.js" id="inception-v3-tf-ov-2021-1-096"></script>
-\endhtmlonly
+## Intel® Xeon® Gold 5218T <a name="xeon-gold"></a>
 
-\htmlonly
-<script src="mobilenet-ssd-cf-ov-2021-1-096.js" id="mobilenet-ssd-cf-ov-2021-1-096"></script>
-\endhtmlonly
+![](img/throughput_xeon_gold.png)
+![](img/value_xeon_gold.png)
+![](img/eff_xeon_gold.png)
 
-\htmlonly
-<script src="mobilenet-v1-1-0-224-tf-ov-2021-1-096.js" id="mobilenet-v1-1-0-224-tf-ov-2021-1-096"></script>
-\endhtmlonly
+---
 
-\htmlonly
-<script src="mobilenet-v2-pytorch-ov-2021-1-096.js" id="mobilenet-v2-pytorch-ov-2021-1-096"></script>
-\endhtmlonly
+## Intel® Xeon® Platinum 8270 <a name="xeon-platinum"></a>
 
-\htmlonly
-<script src="resnet-18-pytorch-ov-2021-1-096.js" id="resnet-18-pytorch-ov-2021-1-096"></script>
-\endhtmlonly
+![](img/throughput_xeon_platinum.png)
+![](img/value_xeon_platinum.png)
+![](img/eff_xeon_platinum.png)
 
-\htmlonly
-<script src="resnet-50-tf-ov-2021-1-096.js" id="resnet-50-tf-ov-2021-1-096"></script>
-\endhtmlonly
+---
 
+## Intel® Atom™ x5-E3940 <a name="atom"></a>
 
-\htmlonly
-<script src="se-resnext-50-cf-ov-2021-1-096.js" id="se-resnext-50-cf-ov-2021-1-096"></script>
-\endhtmlonly
+![](img/throughput_atom.png)
+![](img/value_atom.png)
+![](img/eff_atom.png)
 
-\htmlonly
-<script src="squeezenet1-1-cf-ov-2021-1-096.js" id="squeezenet1-1-cf-ov-2021-1-096"></script>
-\endhtmlonly
+---
 
+## Intel® Core™ i3-8100 <a name="core-i3"></a>
 
-\htmlonly
-<script src="ssd300-cf-ov-2021-1-096.js" id="ssd300-cf-ov-2021-1-096"></script>
-\endhtmlonly
+![](img/throughput_i3.png)
+![](img/value_i3.png)
+![](img/eff_i3.png)
 
-\htmlonly
-<script src="yolo-v3-tf-ov-2021-1-096.js" id="yolo-v3-tf-ov-2021-1-096"></script>
-\endhtmlonly
+---
 
+## Intel® Core™ i5-8500 <a name="core-i5"></a>
+
+![](img/throughput_i5.png)
+![](img/value_i5.png)
+![](img/eff_i5.png)
+
+---
+
+## Intel® Core™ i7-8700T <a name="core-i7"></a>
+
+![](img/throughput_i7.png)
+![](img/value_i7.png)
+![](img/eff_i7.png)
+
+---
+
+## Intel® Core™ i9-10920X <a name="core-i9"></a>
+
+![](img/throughput_i9.png)
+![](img/value_i9.png)
+![](img/eff_i9.png)
+
+---
+
+## Intel® Neural Compute Stick 2 <a name="intel-ncs2"></a>
+
+![](img/throughput_ncs2.png)
+![](img/value_ncs2.png)
+![](img/eff_ncs2.png)
+![](img/benefit_ncs2.png)
+
+---
+
+## Intel® Vision Accelerator Design with Intel® Movidius™ VPUs (Uzel* UI-AR8) <a name="ivad-vpu"></a>
+
+![](img/throughput_hddlr.png)
+![](img/value_hddlr.png)
+![](img/eff_hddlr.png)
+
+---
+
+## Intel® Vision Accelerator Design with Intel® Arria® 10 FPGA
+
+![](img/throughput_ivad_fpga.png)
+![](img/value_ivad_fpga.png)
+![](img/eff_ivad_fpga.png)
 
 ## Platform Configurations
 
-Intel® Distribution of OpenVINO™ toolkit performance benchmark numbers are based on release 2021.1. 
+Intel® Distribution of OpenVINO™ toolkit performance benchmark numbers are based on release 2020.4. 
 
-Intel technologies’ features and benefits depend on system configuration and may require enabled hardware, software or service activation. Learn more at intel.com, or from the OEM or retailer. Performance results are based on testing as of September 25, 2020 and may not reflect all publicly available security updates. See configuration disclosure for details. No product can be absolutely secure. 
+Intel technologies’ features and benefits depend on system configuration and may require enabled hardware, software or service activation. Learn more at intel.com, or from the OEM or retailer. Performance results are based on testing as of July 8, 2020 and may not reflect all publicly available security updates. See configuration disclosure for details. No product can be absolutely secure. 
 
 Software and workloads used in performance tests may have been optimized for performance only on Intel microprocessors. Performance tests, such as SYSmark and MobileMark, are measured using specific computer systems, components, software, operations and functions. Any change to any of those factors may cause the results to vary. You should consult other information and performance tests to assist you in fully evaluating your contemplated purchases, including the performance of that product when combined with other products. For more complete information, see [Performance Benchmark Test Disclosure](https://www.intel.com/content/www/us/en/benchmarks/benchmark.html).
 
@@ -122,31 +142,31 @@ Testing by Intel done on: see test date for each HW platform below.
 | Batch size                      | 1                     | 1                            | 1                            | 1                            |
 | Precision                       | INT8                  | INT8                         | INT8                         | INT8                         |
 | Number of concurrent inference requests | 4             | 32                           | 32                           | 52                           |
-| Test Date                       |  September 25, 2020        |  September 25, 2020               |  September 25, 2020               |  September 25, 2020               |
+| Test Date                       |  July 8, 2020        |  July 8, 2020               |  July 8, 2020               |  July 8, 2020               |
 | Power dissipation, TDP in Watt  | [71](https://ark.intel.com/content/www/us/en/ark/products/134854/intel-xeon-e-2124g-processor-8m-cache-up-to-4-50-ghz.html#tab-blade-1-0-1)                    | [125](https://ark.intel.com/content/www/us/en/ark/products/193394/intel-xeon-silver-4216-processor-22m-cache-2-10-ghz.html#tab-blade-1-0-1)                          | [105](https://ark.intel.com/content/www/us/en/ark/products/193953/intel-xeon-gold-5218t-processor-22m-cache-2-10-ghz.html#tab-blade-1-0-1)             | [205](https://ark.intel.com/content/www/us/en/ark/products/192482/intel-xeon-platinum-8270-processor-35-75m-cache-2-70-ghz.html#tab-blade-1-0-1)                          |
-| CPU Price on September 29, 2020, USD<br>Prices may vary  | [213](https://ark.intel.com/content/www/us/en/ark/products/134854/intel-xeon-e-2124g-processor-8m-cache-up-to-4-50-ghz.html)     | [1,002](https://ark.intel.com/content/www/us/en/ark/products/193394/intel-xeon-silver-4216-processor-22m-cache-2-10-ghz.html)                 | [1,349](https://ark.intel.com/content/www/us/en/ark/products/193953/intel-xeon-gold-5218t-processor-22m-cache-2-10-ghz.html)                        | [7,405](https://ark.intel.com/content/www/us/en/ark/products/192482/intel-xeon-platinum-8270-processor-35-75m-cache-2-70-ghz.html)                        |
+| CPU Price on July 8, 2020, USD<br>Prices may vary  | [213](https://ark.intel.com/content/www/us/en/ark/products/134854/intel-xeon-e-2124g-processor-8m-cache-up-to-4-50-ghz.html)     | [1,002](https://ark.intel.com/content/www/us/en/ark/products/193394/intel-xeon-silver-4216-processor-22m-cache-2-10-ghz.html)                 | [1,349](https://ark.intel.com/content/www/us/en/ark/products/193953/intel-xeon-gold-5218t-processor-22m-cache-2-10-ghz.html)                        | [7,405](https://ark.intel.com/content/www/us/en/ark/products/192482/intel-xeon-platinum-8270-processor-35-75m-cache-2-70-ghz.html)                        |
 
 **CPU Inference Engines (continue)**
 
-|                      | Intel® Core™ i5-8500               | Intel® Core™ i7-8700T               | Intel® Core™ i9-10920X               | 11th Gen Intel® Core™ i5-1145G7E  |
-| -------------------- | ---------------------------------- | ----------------------------------- |--------------------------------------|-----------------------------------|
-| Motherboard          | ASUS* PRIME Z370-A                 | GIGABYTE* Z370M DS3H-CF             | ASUS* PRIME X299-A II                | Intel Corporation<br>internal/Reference Validation Platform |
-| CPU                  | Intel® Core™ i5-8500 CPU @ 3.00GHz | Intel® Core™ i7-8700T CPU @ 2.40GHz | Intel® Core™ i9-10920X CPU @ 3.50GHz | 11th Gen Intel® Core™ i5-1145G7E @ 2.60GHz   |
-| Hyper Threading      | OFF                                | ON                                  | ON                                   | ON                                           |
-| Turbo Setting        | ON                                 | ON                                  | ON                                   | ON                                           |
-| Memory               | 2 x 16 GB DDR4 2666MHz             | 4 x 16 GB DDR4 2400MHz              | 4 x 16 GB DDR4 2666MHz               | 2 x 8 GB DDR4 3200MHz                        |
-| Operating System     | Ubuntu* 18.04 LTS                  | Ubuntu* 18.04 LTS                   | Ubuntu* 18.04 LTS                    | Ubuntu* 18.04 LTS                            |
-| Kernel Version       | 5.3.0-24-generic                   | 5.0.0-23-generic                    | 5.0.0-23-generic                     | 5.8.0-05-generic                             |
-| BIOS Vendor          | American Megatrends Inc.*          | American Megatrends Inc.*           | American Megatrends Inc.*            | Intel Corporation                            |
-| BIOS Version         | 2401                               | F11                                 | 505                                  | TGLIFUI1.R00.3243.A04.2006302148             |
-| BIOS Release         | July 12, 2019                      | March 13, 2019                      | December 17, 2019                    | June 30, 2020                                |
-| BIOS Settings        | Select optimized default settings, <br>save & exit | Select optimized default settings, <br>set OS type to "other", <br>save & exit | Default Settings | Default Settings |
-| Batch size           | 1                                  | 1                                   | 1                                    | 1                                            |
-| Precision            | INT8                               | INT8                                | INT8                                 | INT8                                         |
-| Number of concurrent inference requests | 3               | 4                                   | 24                                   | 4                                            |
-| Test Date            | September 25, 2020                 | September 25, 2020                  | September 25, 2020                   | September 25, 2020                           |
-| Power dissipation, TDP in Watt | [65](https://ark.intel.com/content/www/us/en/ark/products/129939/intel-core-i5-8500-processor-9m-cache-up-to-4-10-ghz.html#tab-blade-1-0-1)                                 | [35](https://ark.intel.com/content/www/us/en/ark/products/129948/intel-core-i7-8700t-processor-12m-cache-up-to-4-00-ghz.html#tab-blade-1-0-1) | [165](https://ark.intel.com/content/www/us/en/ark/products/198012/intel-core-i9-10920x-x-series-processor-19-25m-cache-3-50-ghz.html) | [28](https://ark.intel.com/content/www/us/en/ark/products/208081/intel-core-i5-1145g7e-processor-8m-cache-up-to-4-10-ghz.html) |
-| CPU Price on September 29, 2020, USD<br>Prices may vary  | [192](https://ark.intel.com/content/www/us/en/ark/products/129939/intel-core-i5-8500-processor-9m-cache-up-to-4-10-ghz.html)                               | [303](https://ark.intel.com/content/www/us/en/ark/products/129948/intel-core-i7-8700t-processor-12m-cache-up-to-4-00-ghz.html)                                 | [700](https://ark.intel.com/content/www/us/en/ark/products/198012/intel-core-i9-10920x-x-series-processor-19-25m-cache-3-50-ghz.html) | [309](https://mysamples.intel.com/SAM_U_Product/ProductDetail.aspx?InputMMID=99A3D1&RequestID=0&ProductID=1213750) |
+|                      | Intel® Core™ i5-8500               | Intel® Core™ i7-8700T               | Intel® Core™ i9-10920X               |
+| -------------------- | ---------------------------------- | ----------------------------------- |--------------------------------------|
+| Motherboard          | ASUS* PRIME Z370-A                 | GIGABYTE* Z370M DS3H-CF             | ASUS* PRIME X299-A II                |
+| CPU                  | Intel® Core™ i5-8500 CPU @ 3.00GHz | Intel® Core™ i7-8700T CPU @ 2.40GHz | Intel® Core™ i9-10920X CPU @ 3.50GHz |
+| Hyper Threading      | OFF                                | ON                                  | ON                                   |
+| Turbo Setting        | ON                                 | ON                                  | ON                                   |
+| Memory               | 2 x 16 GB DDR4 2666MHz             | 4 x 16 GB DDR4 2400MHz              | 4 x 16 GB DDR4 2666MHz               |
+| Operating System     | Ubuntu* 18.04 LTS                  | Ubuntu* 18.04 LTS                   | Ubuntu* 18.04 LTS                    |
+| Kernel Version       | 5.3.0-24-generic                   | 5.0.0-23-generic                    | 5.0.0-23-generic                     |
+| BIOS Vendor          | American Megatrends Inc.*          | American Megatrends Inc.*           | American Megatrends Inc.*            |
+| BIOS Version         | 2401                               | F11                                 | 505                                  |
+| BIOS Release         | July 12, 2019                      | March 13, 2019                      | December 17, 2019                    |
+| BIOS Settings        | Select optimized default settings, <br>save & exit | Select optimized default settings, <br>set OS type to "other", <br>save & exit | Default Settings |
+| Batch size           | 1                                  | 1                                   | 1                                    |
+| Precision            | INT8                               | INT8                                | INT8                                 |
+| Number of concurrent inference requests | 3               | 4                                   | 24                                   |
+| Test Date            | July 8, 2020                       | July 8, 2020                       | July 8, 2020                        |
+| Power dissipation, TDP in Watt | [65](https://ark.intel.com/content/www/us/en/ark/products/129939/intel-core-i5-8500-processor-9m-cache-up-to-4-10-ghz.html#tab-blade-1-0-1)                                 | [35](https://ark.intel.com/content/www/us/en/ark/products/129948/intel-core-i7-8700t-processor-12m-cache-up-to-4-00-ghz.html#tab-blade-1-0-1) | [165](https://ark.intel.com/content/www/us/en/ark/products/198012/intel-core-i9-10920x-x-series-processor-19-25m-cache-3-50-ghz.html) |
+| CPU Price on  July 8, 2020, USD<br>Prices may vary  | [192](https://ark.intel.com/content/www/us/en/ark/products/129939/intel-core-i5-8500-processor-9m-cache-up-to-4-10-ghz.html)                               | [303](https://ark.intel.com/content/www/us/en/ark/products/129948/intel-core-i7-8700t-processor-12m-cache-up-to-4-00-ghz.html)                                 | [700](https://ark.intel.com/content/www/us/en/ark/products/198012/intel-core-i9-10920x-x-series-processor-19-25m-cache-3-50-ghz.html)
 
 **CPU Inference Engines (continue)**
 
@@ -166,37 +186,37 @@ Testing by Intel done on: see test date for each HW platform below.
 | Batch size           | 1                                     | 1                                  |
 | Precision            | INT8                                  | INT8                               |
 | Number of concurrent inference requests | 4                  | 4                                  |
-| Test Date            | September 25, 2020                         | September 25, 2020                      |
+| Test Date            | July 8, 2020                         | July 8, 2020                      |
 | Power dissipation, TDP in Watt | [9.5](https://ark.intel.com/content/www/us/en/ark/products/96485/intel-atom-x5-e3940-processor-2m-cache-up-to-1-80-ghz.html)                                                              | [65](https://ark.intel.com/content/www/us/en/ark/products/126688/intel-core-i3-8100-processor-6m-cache-3-60-ghz.html#tab-blade-1-0-1)|
-| CPU Price on September 29, 2020, USD<br>Prices may vary  | [34](https://ark.intel.com/content/www/us/en/ark/products/96485/intel-atom-x5-e3940-processor-2m-cache-up-to-1-80-ghz.html)                                                        | [117](https://ark.intel.com/content/www/us/en/ark/products/126688/intel-core-i3-8100-processor-6m-cache-3-60-ghz.html)       |
+| CPU Price on  July 8, 2020, USD<br>Prices may vary  | [34](https://ark.intel.com/content/www/us/en/ark/products/96485/intel-atom-x5-e3940-processor-2m-cache-up-to-1-80-ghz.html)                                                        | [117](https://ark.intel.com/content/www/us/en/ark/products/126688/intel-core-i3-8100-processor-6m-cache-3-60-ghz.html)       |
 
 
 
 **Accelerator Inference Engines**
 
-|                                         | Intel® Neural Compute Stick 2         | Intel® Vision Accelerator Design<br>with Intel® Movidius™ VPUs (Uzel* UI-AR8) | 
-| --------------------------------------- | ------------------------------------- | ------------------------------------- |
-| VPU                                     | 1 X Intel® Movidius™ Myriad™ X MA2485 | 8 X Intel® Movidius™ Myriad™ X MA2485 |
-| Connection                              | USB 2.0/3.0                           | PCIe X4                               |
-| Batch size                              | 1                                     | 1                                     |
-| Precision                               | FP16                                  | FP16                                  |
-| Number of concurrent inference requests | 4                                     | 32                                    |
-| Power dissipation, TDP in Watt          | 2.5                                   | [30](https://www.mouser.com/ProductDetail/IEI/MUSTANG-V100-MX8-R10?qs=u16ybLDytRaZtiUUvsd36w%3D%3D)          |
-| CPU Price, USD<br>Prices may vary | [69](https://ark.intel.com/content/www/us/en/ark/products/140109/intel-neural-compute-stick-2.html) (from September 29, 2020) | [768](https://www.mouser.com/ProductDetail/IEI/MUSTANG-V100-MX8-R10?qs=u16ybLDytRaZtiUUvsd36w%3D%3D) (from May 15, 2020)                           |
-| Host Computer                           | Intel® Core™ i7                       | Intel® Core™ i5                       |
-| Motherboard                             | ASUS* Z370-A II                       | Uzelinfo* / US-E1300                  |
-| CPU                                     | Intel® Core™ i7-8700 CPU @ 3.20GHz    | Intel® Core™ i5-6600 CPU @ 3.30GHz    |
-| Hyper Threading                         | ON                                    | OFF                                   |
-| Turbo Setting                           | ON                                    | ON                                    |
-| Memory                                  | 4 x 16 GB DDR4 2666MHz                | 2 x 16 GB DDR4 2400MHz                |
-| Operating System                        | Ubuntu* 18.04 LTS                     | Ubuntu* 18.04 LTS                     |
-| Kernel Version                          | 5.0.0-23-generic                      | 5.0.0-23-generic                      |
-| BIOS Vendor                             | American Megatrends Inc.*             | American Megatrends Inc.*             |
-| BIOS Version                            | 411                                   | 5.12                                  |
-| BIOS Release                            | September 21, 2018                    | September 21, 2018                    |
-| Test Date                               | September 25, 2020                    | September 25, 2020                    |        
+|              | Intel® Neural Compute Stick 2 | Intel® Vision Accelerator Design<br>with Intel® Movidius™ VPUs (Uzel* UI-AR8) | Intel® Vision Accelerator Design<br>with Intel® Arria® 10 FPGA - IEI/SAF3*| 
+| --------------------                    | ------------------------------------- | ------------------------------------- | ------------------------- |
+| VPU                                     | 1 X Intel® Movidius™ Myriad™ X MA2485 | 8 X Intel® Movidius™ Myriad™ X MA2485 | 1 X Intel® Arria® 10 FPGA |
+| Connection                              | USB 2.0/3.0                           | PCIe X4                               | PCIe X8                   |
+| Batch size                              | 1                                     | 1                                     | 1                         |
+| Precision                               | FP16                                  | FP16                                  | FP11                      |
+| Number of concurrent inference requests | 4                                     | 32                                    | 5                         |
+| Power dissipation, TDP in Watt          | 2.5                                   | [30](https://www.mouser.com/ProductDetail/IEI/MUSTANG-V100-MX8-R10?qs=u16ybLDytRaZtiUUvsd36w%3D%3D)                                    | [60](https://www.mouser.com/ProductDetail/IEI/MUSTANG-F100-A10-R10?qs=sGAEpiMZZMtNlGR3Dbecs5Qs0RmP5oxxCbTJPjyRuMXthliRUwiVGw%3D%3D) |
+| CPU Price, USD<br>Prices may vary | [69](https://ark.intel.com/content/www/us/en/ark/products/140109/intel-neural-compute-stick-2.html) (from July 8, 2020)                                | [768](https://www.mouser.com/ProductDetail/IEI/MUSTANG-V100-MX8-R10?qs=u16ybLDytRaZtiUUvsd36w%3D%3D) (from May 15, 2020)               | [1,650](https://www.bhphotovideo.com/c/product/1477989-REG/qnap_mustang_f100_a10_r10_pcie_fpga_highest_performance.html/?ap=y&ap=y&smp=y&msclkid=371b373256dd1a52beb969ecf5981bf8) (from July 8, 2020)                    |
+| Host Computer                           | Intel® Core™ i7                       | Intel® Core™ i5                       | Intel® Xeon® E3        |
+| Motherboard                             | ASUS* Z370-A II                       | Uzelinfo* / US-E1300                  | IEI/SAF3*              |
+| CPU                                     | Intel® Core™ i7-8700 CPU @ 3.20GHz    | Intel® Core™ i5-6600 CPU @ 3.30GHz    | Intel® Xeon® CPU E3-1268L v5 @ 2.40GHz |
+| Hyper Threading                         | ON                                    | OFF                                   | OFF                       |
+| Turbo Setting                           | ON                                    | ON                                    | ON                        |
+| Memory                                  | 4 x 16 GB DDR4 2666MHz                | 2 x 16 GB DDR4 2400MHz                | 2 x 16 GB DDR4 2666MHz    |
+| Operating System                        | Ubuntu* 18.04 LTS                     | Ubuntu* 18.04 LTS                     | Ubuntu* 16.04 LTS         |
+| Kernel Version                          | 5.0.0-23-generic                      | 5.0.0-23-generic                      | 4.13.0-45-generic         |
+| BIOS Vendor                             | American Megatrends Inc.*             | American Megatrends Inc.*             | American Megatrends Inc.* |
+| BIOS Version                            | 411                                   | 5.12                                  | V2RMAR15                  |
+| BIOS Release                            | September 21, 2018                    | September 21, 2018                    | December 03, 2019         |
+| Test Date                               | July 8, 2020                         | July 8, 2020                         | July 8, 2020          |
 
-Please follow this link for more detailed configuration descriptions: [Configuration Details](https://docs.openvinotoolkit.org/resources/benchmark_files/system_configurations_2021.1.html)
+Please follow this link for more detailed configuration descriptions: [Configuration Details](https://docs.openvinotoolkit.org/resources/benchmark_files/system_configurations_2020.4.html)
 
 \htmlonly
 <style>

docs/benchmarks/performance_benchmarks_faq.md

@@ -1,6 +1,6 @@
 # Performance Information Frequently Asked Questions {#openvino_docs_performance_benchmarks_faq}
 
-The following questions and answers are related to [performance benchmarks](./performance_benchmarks.md) published on the documentation site.
+The following questions and answers are related to performance benchmarks published on the [Performance Information](https://docs.openvinotoolkit.org/latest/_docs_performance_benchmarks.html) documentation site.
 
 #### 1. How often do performance benchmarks get updated?
 New performance benchmarks are typically published on every `major.minor` release of the Intel® Distribution of OpenVINO™ toolkit.
@@ -15,53 +15,36 @@ The models used in the performance benchmarks were chosen based on general adopt
 CF means Caffe*, while TF means TensorFlow*.
 
 #### 5. How can I run the benchmark results on my own?
-All of the performance benchmarks were generated using the open-sourced tool within the Intel® Distribution of OpenVINO™ toolkit called `benchmark_app`, which is available in both [C++](../../inference-engine/samples/benchmark_app/README.md) and [Python](../../inference-engine/tools/benchmark_tool/README.md). 
+All of the performance benchmarks were generated using the open-sourced tool within the Intel® Distribution of OpenVINO™ toolkit called `benchmark_app`, which is available in both [C++](https://docs.openvinotoolkit.org/latest/_inference_engine_samples_benchmark_app_README.html) and [Python](https://docs.openvinotoolkit.org/latest/_inference_engine_tools_benchmark_tool_README.html).
 
 #### 6. What image sizes are used for the classification network models?
 The image size used in the inference depends on the network being benchmarked. The following table shows the list of input sizes for each network model.
 |   **Model**																														 |   **Public Network**                    |     **Task**                | **Input Size** (Height x Width)   |
 |------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------|-----------------------------|-----------------------------------|
-|    [bert-large-uncased-whole-word-masking-squad](https://github.com/opencv/open_model_zoo/tree/develop/models/intel/bert-large-uncased-whole-word-masking-squad-int8-0001)   | 	BERT-large	|question / answer	|384|
-|    [deeplabv3-TF](https://github.com/opencv/open_model_zoo/tree/master/models/public/deeplabv3)                                    |	  DeepLab v3 Tf	                       |semantic segmentation	     |    513x513                          |
-|    [densenet-121-TF](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/densenet-121-tf)                  | 	  Densenet-121 Tf	                   |classification	    |    224x224                 |
-|    [facenet-20180408-102900-TF](https://github.com/opencv/open_model_zoo/tree/master/models/public/facenet-20180408-102900)        |    FaceNet TF                           |    face recognition         |    160x160                        |
-|    [faster_rcnn_resnet50_coco-TF](https://github.com/opencv/open_model_zoo/tree/master/models/public/faster_rcnn_resnet50_coco)    |    Faster RCNN Tf                       |    object detection           |    600x1024					     |
-|    [googlenet-v1-TF](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/googlenet-v1-tf)				     |    GoogLeNet_ILSVRC-2012                |    classification           |    224x224				  |
-|    [inception-v3-TF](https://github.com/opencv/open_model_zoo/tree/master/models/public/googlenet-v3)								 |    Inception v3 Tf                      |    classification           |    299x299				  |
-|    [mobilenet-ssd-CF](https://github.com/opencv/open_model_zoo/tree/master/models/public/mobilenet-ssd)						     |    SSD (MobileNet)_COCO-2017_Caffe      |    object detection         |    300x300						 |
-|    [mobilenet-v1-1.0-224-TF](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/mobilenet-v1-1.0-224-tf)  |    MobileNet v1 Tf                      |    classification    |    224x224                        |
+|    [faster_rcnn_resnet50_coco-TF](https://github.com/opencv/open_model_zoo/tree/master/models/public/faster_rcnn_resnet50_coco)    |    Faster RCNN Tf                       |    object detection         |    600x1024					     |
+|    [googlenet-v1-CF](https://github.com/opencv/open_model_zoo/tree/master/models/public/googlenet-v1)							     |    GoogLeNet_ILSVRC-2012_Caffe          |    classification           |    224x224				         |
+|    [googlenet-v3-TF](https://github.com/opencv/open_model_zoo/tree/master/models/public/googlenet-v3)								 |    Inception v3 Tf                      |    classification           |    299x299				   	     |
+|    [mobilenet-ssd-CF](https://github.com/opencv/open_model_zoo/tree/master/models/public/mobilenet-ssd)						     |    SSD   (MobileNet)_COCO-2017_Caffe    |    object detection         |    300x300						 |
 |    [mobilenet-v2-1.0-224-TF](https://github.com/opencv/open_model_zoo/tree/master/models/public/mobilenet-v2-1.0-224)			     |    MobileNet v2 Tf                      |    classification           |    224x224						 |
-|    [mobilenet-v2-pytorch](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/mobilenet-v2-pytorch )		 |    Mobilenet V2 PyTorch                 |    classification           |    224x224					     |
-|    [resnet-18-pytorch](https://github.com/opencv/open_model_zoo/tree/master/models/public/resnet-18-pytorch)		  			     |    ResNet-18 PyTorch                    |    classification           |    224x224						 |
-|    [resnet-50-pytorch](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/resnet-50-pytorch)              |    ResNet-50 v1 PyTorch                 |    classification           |    224x224                        |
-|    [resnet-50-TF](https://github.com/opencv/open_model_zoo/tree/master/models/public/resnet-50-tf)								 |    ResNet-50_v1_ILSVRC-2012             |    classification           |    224x224						 |
+|    [mobilenet-v2-CF](https://github.com/opencv/open_model_zoo/tree/master/models/public/mobilenet-v2)								 |    Mobilenet V2 Caffe                   |    classification           |    224x224					     |
+|    [resnet-101-CF](https://github.com/opencv/open_model_zoo/tree/master/models/public/resnet-101)									 |    ResNet-101_ILSVRC-2012_Caffe         |    classification           |    224x224						 |
+|    [resnet-50-CF](https://github.com/opencv/open_model_zoo/tree/master/models/public/resnet-50)									 |    ResNet-50_v1_ILSVRC-2012_Caffe       |    classification           |    224x224						 |
 |    [se-resnext-50-CF](https://github.com/opencv/open_model_zoo/tree/master/models/public/se-resnext-50)						     |    Se-ResNext-50_ILSVRC-2012_Caffe      |    classification           |    224x224						 |
 |    [squeezenet1.1-CF](https://github.com/opencv/open_model_zoo/tree/master/models/public/squeezenet1.1)						     |    SqueezeNet_v1.1_ILSVRC-2012_Caffe    |    classification           |    227x227						 |
 |    [ssd300-CF](https://github.com/opencv/open_model_zoo/tree/master/models/public/ssd300)										     |    SSD (VGG-16)_VOC-2007_Caffe          |    object detection         |    300x300						 |
-|    [yolo_v3-TF](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/yolo-v3-tf)                            | 	  TF Keras YOLO v3 Modelset            |	 object detection	      |    300x300                        |
-|    [ssd_mobilenet_v1_coco-TF](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/ssd_mobilenet_v1_coco)   |    ssd_mobilenet_v1_coco                |    object detection         |    300x300                        |
-|    [ssdlite_mobilenet_v2-TF](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/ssdlite_mobilenet_v2)     |    ssd_mobilenet_v2                     |    object detection         |    300x300                        |
 
 #### 7. Where can I purchase the specific hardware used in the benchmarking?
-Intel partners with various vendors all over the world. Visit the [Intel® AI: In Production Partners & Solutions Catalog](https://www.intel.com/content/www/us/en/internet-of-things/ai-in-production/partners-solutions-catalog.html) for a list of Equipment Makers and the [Supported Devices](../IE_DG/supported_plugins/Supported_Devices.md) documentation. You can also remotely test and run models before purchasing any hardware by using [Intel® DevCloud for the Edge](http://devcloud.intel.com/edge/).
+Intel partners with various vendors all over the world. Visit the [Intel® AI: In Production Partners & Solutions Catalog](https://www.intel.com/content/www/us/en/internet-of-things/ai-in-production/partners-solutions-catalog.html) for a list of Equipment Makers and the [Supported Devices](https://docs.openvinotoolkit.org/latest/_docs_IE_DG_supported_plugins_Supported_Devices.html) documentation. You can also remotely test and run models before purchasing any hardware by using [Intel® DevCloud for the Edge](http://devcloud.intel.com/edge/).
 
 #### 8. How can I optimize my models for better performance or accuracy?
-We published a set of guidelines and recommendations to optimize your models available in an [introductory](../IE_DG/Intro_to_Performance.md) guide and an [advanced](../optimization_guide/dldt_optimization_guide.md) guide. For further support, please join the conversation in the [Community Forum](https://software.intel.com/en-us/forums/intel-distribution-of-openvino-toolkit).
+We published a set of guidelines and recommendations to optimize your models available in an [introductory](https://docs.openvinotoolkit.org/latest/_docs_IE_DG_Intro_to_Performance.html) guide and an [advanced](https://docs.openvinotoolkit.org/latest/_docs_optimization_guide_dldt_optimization_guide.html) guide. For further support, please join the conversation in the [Community Forum](https://software.intel.com/en-us/forums/intel-distribution-of-openvino-toolkit).
 
 #### 9. Why are INT8 optimized models used for benchmarking on CPUs with no VNNI support?
-The benefit of low-precision optimization using the OpenVINO™ toolkit model optimizer extends beyond processors supporting VNNI through Intel® DL Boost. The reduced bit width of INT8 compared to FP32 allows Intel® CPU to process the data faster and thus offers better throughput on any converted model agnostic of the intrinsically supported low-precision optimizations within Intel® hardware. Please refer to [INT8 vs. FP32 Comparison on Select Networks and Platforms](./performance_int8_vs_fp32.html) for comparison on boost factors for different network models and a selection of Intel® CPU architectures, including AVX-2 with Intel® Core™ i7-8700T, and AVX-512 (VNNI) with Intel® Xeon® 5218T and Intel® Xeon® 8270.
+The benefit of low-precision optimization using the OpenVINO™ toolkit model optimizer extends beyond processors supporting VNNI through Intel® DL Boost. The reduced bit width of INT8 compared to FP32 allows Intel® CPU to process the data faster and thus offers better throughput on any converted model agnostic of the intrinsically supported low-precision optimizations within Intel® hardware. Please refer to [INT8 vs. FP32 Comparison on Select Networks and Platforms](https://docs.openvinotoolkit.org/latest/_docs_performance_int8_vs_fp32.html) for comparison on boost factors for different network models and a selection of Intel® CPU architectures, including AVX-2 with Intel® Core™ i7-8700T, and AVX-512 (VNNI) with Intel® Xeon® 5218T and Intel® Xeon® 8270.
 
-#### 10. Previous releases included benchmarks on googlenet-v1-CF (Caffe). Why is there no longer benchmarks on this neural network model?
-We replaced googlenet-v1-CF to resnet-18-pytorch due to changes in developer usage. The public model resnet-18 is used by many developers as an Image Classification model. This pre-optimized model was also trained on the ImageNet database, similar to googlenet-v1-CF. Both googlenet-v1-CF and resnet-18 will remain part of the Open Model Zoo. Developers are encouraged to utilize resnet-18-pytorch for Image Classification use cases.
+#### 10. Previous releases included benchmarks on googlenet-v1. Why is there no longer benchmarks on this neural network model?
+We replaced googlenet-v1 to [resnet-18-pytorch](https://github.com/opencv/open_model_zoo/blob/master/models/public/resnet-18-pytorch/resnet-18-pytorch.md) due to changes in developer usage. The public model resnet-18 is used by many developers as an Image Classification model. This pre-optimized model was also trained on the ImageNet database, similar to googlenet-v1. Both googlenet-v1 and resnet-18 will remain part of the Open Model Zoo. Developers are encouraged to utilize resnet-18-pytorch for Image Classification use cases.
 
-#### 11. Why have resnet-50-CF, mobilenet-v1-1.0-224-CF, mobilenet-v2-CF and resnet-101-CF been removed?
-The CAFFE version of resnet-50, mobilenet-v1-1.0-224 and mobilenet-v2 have been replaced with their TensorFlow and PyTorch counterparts. Resnet-50-CF is replaced by resnet-50-TF, mobilenet-v1-1.0-224-CF is replaced by mobilenet-v1-1.0-224-TF and mobilenet-v2-CF is replaced by mobilenetv2-PyTorch. Resnet-50-CF an resnet-101-CF are no longer maintained at their public source repos.
-
-#### 12. Where can I search for OpenVINO™ performance results based on HW-platforms?
-The web site format has changed in order to support the more common search approach of looking for the performance of a given neural network model on different HW-platforms. As opposed to review a given HW-platform's performance on different neural network models.
-
-#### 13. How is Latency measured?
-Latency is measured by running the OpenVINO™ inference engine in synchronous mode. In synchronous mode each frame or image is processed through the entire set of stages (pre-processing, inference, post-processing) before the next frame or image is processed. This KPI is relevant for applications where the inference on a single image is required, for example the analysis of an ultra sound image in a medical application or the analysis of a seismic image in the oil & gas industry. Other use cases include real-time or near real-time applications like an industrial robot's response to changes in its environment and obstacle avoidance for autonomous vehicles where a quick response to the result of the inference is required.
 
 \htmlonly
 <style>

docs/benchmarks/performance_int8_vs_fp32.md

@@ -10,7 +10,6 @@ The table below illustrates the speed-up factor for the performance gain by swit
     <th>Intel® Xeon® <br>Gold <br>5218T</th>
     <th>Intel® Xeon® <br>Platinum <br>8270</th>
     <th>Intel® Core™ <br>i7-1065G7</th>
-    <th>Intel® Core™ <br>i5-1145G7E</th>
   </tr>
   <tr align="left">
     <th>OpenVINO <br>benchmark <br>model name</th>
@@ -20,110 +19,106 @@ The table below illustrates the speed-up factor for the performance gain by swit
   <tr>
     <td>bert-large-<br>uncased-whole-word-<br>masking-squad-0001</td>
     <td>SQuAD</td>
-    <td>1.6</td>
+    <td>1.5</td>
     <td>2.5</td>
     <td>2.0</td>
     <td>N/A</td>
-    <td>2.8</td>
   </tr>
   <tr>
-    <td>brain-tumor-<br>segmentation-<br>0001-MXNET</td>
+    <td>brain-tumor-<br>segmentation-<br>0001-mxnet</td>
     <td>BraTS</td>
     <td>1.5</td>
     <td>1.7</td>
     <td>1.6</td>
     <td>1.9</td>
-    <td>1.8</td>
   </tr>
   <tr>
-    <td>deeplabv3-TF</td>
-    <td>VOC 2012<br>Segmentation</td>
+    <td>brain-tumor-<br>segmentation-<br>0002-cf2</td>
+    <td>BraTS<br>2017</td>
+    <td>1.2</td>
+    <td>1.7</td>
     <td>1.4</td>
+    <td>2.2</td>
+  </tr>
+  <tr>
+    <td>deeplabv3-tf</td>
+    <td>VOC 2012<br>Segmentation</td>
+    <td>1.5</td>
     <td>2.4</td>
     <td>2.6</td>
     <td>2.8</td>
-    <td>2.9</td>
   </tr>
   <tr>
-    <td>densenet-121-TF</td>
-    <td>ImageNet</td>
-    <td>1.6</td>
-    <td>3.2</td>
-    <td>3.2</td>
-    <td>3.0</td>
-    <td>3.2</td>
-  </tr>
-  <tr>
-    <td>facenet-<br>20180408-<br>102900-TF</td>
+    <td>facenet-<br>20180408-<br>102900-tf</td>
     <td>LFW</td>
     <td>2.0</td>
-    <td>3.6</td>
     <td>3.5</td>
-    <td>3.2</td>
+    <td>3.5</td>
     <td>3.5</td>
   </tr>
   <tr>
-    <td>faster_rcnn_<br>resnet50_coco-TF</td>
+    <td>faster_rcnn_<br>resnet50_coco-tf</td>
     <td>MS COCO</td>
     <td>1.7</td>
-    <td>3.5</td>
+    <td>3.4</td>
     <td>3.4</td>
     <td>3.6</td>
-    <td>3.6</td>
   </tr>
   <tr>
-    <td>googlenet-v1-TF</td>
+    <td>googlenet-v1-caffe</td>
     <td>ImageNet</td>
-    <td>1.8</td>
-    <td>3.6</td>
-    <td>3.7</td>
-    <td>3.5</td>
-    <td>3.6</td>
+    <td>1.5</td>
+    <td>2.9</td>
+    <td>2.2</td>
+    <td>3.2</td>
   </tr>
   <tr>
-    <td>inception-v3-TF</td>
+    <td>inception-v3-tf</td>
     <td>ImageNet</td>
     <td>1.8</td>
     <td>3.8</td>
     <td>4.0</td>
     <td>3.7</td>
-    <td>3.7</td>
   </tr>
   <tr>
-    <td>mobilenet-<br>ssd-CF</td>
+    <td>mobilenet-<br>ssd-caffe</td>
     <td>VOC2012</td>
     <td>1.5</td>
     <td>3.0</td>
     <td>3.3</td>
     <td>3.1</td>
-    <td>3.3</td>
   </tr>
   <tr>
-    <td>mobilenet-v1-1.0-<br>224-TF</td>
+    <td>mobilenet-v1-1.0-<br>224-caffe</td>
     <td>ImageNet</td>
     <td>1.5</td>
     <td>3.2</td>
     <td>3.9</td>
     <td>2.9</td>
-    <td>3.2</td>
   </tr>
   <tr>
-    <td>mobilenet-v2-1.0-<br>224-TF</td>
+    <td>mobilenet-v2-1.0-<br>224-tf</td>
     <td>ImageNet</td>
     <td>1.3</td>
-    <td>2.7</td>
+    <td>2.6</td>
     <td>3.8</td>
     <td>2.2</td>
-    <td>2.5</td>
   </tr>
   <tr>
-    <td>mobilenet-v2-<br>pytorch</td>
+    <td>mobilenet-v2-<br>caffe</td>
     <td>ImageNet</td>
-    <td>1.4</td>
-    <td>2.6</td>
+    <td>1.3</td>
+    <td>2.5</td>
+    <td>3.4</td>
+    <td>2.2</td>
+  </tr>
+  <tr>
+    <td>resnet-101-<br>caffe</td>
+    <td>ImageNet</td>
+    <td>1.8</td>
+    <td>3.7</td>
+    <td>3.7</td>
     <td>3.6</td>
-    <td>2.3</td>
-    <td>2.4</td>
   </tr>
   <tr>
     <td>resnet-18-<br>pytorch</td>
@@ -132,70 +127,54 @@ The table below illustrates the speed-up factor for the performance gain by swit
     <td>3.7</td>
     <td>3.8</td>
     <td>3.6</td>
+  </tr>
+  <tr>
+    <td>resnet-50-<br>caffe</td>
+    <td>ImageNet</td>
+    <td>1.8</td>
     <td>3.6</td>
+    <td>3.9</td>
+    <td>3.5</td>
   </tr>
   <tr>
     <td>resnet-50-<br>pytorch</td>
     <td>ImageNet</td>
     <td>1.8</td>
     <td>3.6</td>
-    <td>3.8</td>
+    <td>3.9</td>
     <td>3.5</td>
-    <td>3.6</td>
   </tr>
   <tr>
-    <td>resnet-50-<br>TF</td>
-    <td>ImageNet</td>
-    <td>1.8</td>
-    <td>3.5</td>
-    <td>3.8</td>
-    <td>3.4</td>
-    <td>4.0</td>
-  </tr>
-  <tr>
-    <td>squeezenet1.1-<br>CF</td>
+    <td>squeezenet1.1-<br>caffe</td>
     <td>ImageNet</td>
     <td>1.6</td>
     <td>2.9</td>
     <td>3.2</td>
     <td>3.0</td>
-    <td>3.2</td>
   </tr>
   <tr>
     <td>ssd_mobilenet_<br>v1_coco-tf</td>
-    <td>VOC2012</td>
+    <td>MS COCO</td>
     <td>1.6</td>
     <td>3.0</td>
     <td>3.4</td>
     <td>3.1</td>
-    <td>3.3</td>
   </tr>
   <tr>
-    <td>ssd300-CF</td>
+    <td>ssd300-caffe</td>
     <td>MS COCO</td>
     <td>1.8</td>
     <td>3.7</td>
-    <td>3.6</td>
+    <td>3.7</td>
     <td>3.8</td>
-    <td>4.0</td>
   </tr>
   <tr>
-    <td>ssdlite_<br>mobilenet_<br>v2-TF</td>
+    <td>ssdlite_<br>mobilenet_<br>v2-tf</td>
     <td>MS COCO</td>
     <td>1.4</td>
     <td>2.3</td>
-    <td>3.1</td>
+    <td>3.0</td>
     <td>2.4</td>
-    <td>2.6</td>
-  </tr>
-  <tr>
-    <td>yolo_v3-TF</td>
-    <td>MS COCO</td>
-    <td>1.8</td>
-    <td>3.8</td>
-    <td>3.9</td>
-    <td>3.7</td>
-    <td>3.8</td>
   </tr>
 </table>
 
@@ -220,60 +199,60 @@ The following table shows the absolute accuracy drop that is calculated as the d
     <td>bert-large-<br>uncased-whole-word-<br>masking-squad-0001</td>
     <td>SQuAD</td>
     <td>F1</td>
-    <td>0.65</td>
-    <td>0.57</td>
-    <td>0.83</td>
+    <td>0.46</td>
+    <td>0.70</td>
+    <td>0.64</td>
   </tr>
   <tr>
-    <td>brain-tumor-<br>segmentation-<br>0001-MXNET</td>
+    <td>brain-tumor-<br>segmentation-<br>0001-mxnet</td>
     <td>BraTS</td>
     <td>Dice-index@ <br>Mean@ <br>Overall Tumor</td>
     <td>0.08</td>
     <td>0.08</td>
-    <td>0.9</td>
+    <td>0.14</td>
   </tr>
   <tr>
-    <td>deeplabv3-TF</td>
+    <td>brain-tumor-<br>segmentation-<br>0002-cf2</td>
+    <td>BraTS<br>2017</td>
+    <td>Dice-index@ <br>Mean@ <br>Overall Tumor</td>
+    <td>0.16</td>
+    <td>0.14</td>
+    <td>0.13</td>
+  </tr>
+  <tr>
+    <td>deeplabv3-tf</td>
     <td>VOC 2012<br>Segmentation</td>
     <td>mean_iou</td>
-    <td>0.73</td>
-    <td>0.73</td>
-    <td>1.11</td>
+    <td>0.28</td>
+    <td>0.71</td>
+    <td>0.71</td>
   </tr>
   <tr>
-    <td>densenet-121-TF</td>
-    <td>ImageNet</td>
-    <td>acc@top-1</td>
-    <td>0.74</td>
-    <td>0.74</td>
-    <td>0.76</td>
-  </tr>
-  <tr>
-    <td>facenet-<br>20180408-<br>102900-TF</td>
+    <td>facenet-<br>20180408-<br>102900-tf</td>
     <td>LFW</td>
     <td>pairwise_<br>accuracy<br>_subsets</td>
     <td>0.02</td>
-    <td>0.02</td>
-    <td>0.02</td>
+    <td>0.05</td>
+    <td>0.05</td>
   </tr>
   <tr>
-    <td>faster_rcnn_<br>resnet50_coco-TF</td>
+    <td>faster_rcnn_<br>resnet50_coco-tf</td>
     <td>MS COCO</td>
     <td>coco_<br>precision</td>
     <td>0.21</td>
-    <td>0.21</td>
+    <td>0.20</td>
     <td>0.20</td>
   </tr>
   <tr>
-    <td>googlenet-v1-TF</td>
+    <td>googlenet-v1-caffe</td>
     <td>ImageNet</td>
     <td>acc@top-1</td>
-    <td>0.03</td>
-    <td>0.03</td>
-    <td>0.01</td>
+    <td>0.24</td>
+    <td>0.19</td>
+    <td>0.20</td>
   </tr>
   <tr>
-    <td>inception-v3-TF</td>
+    <td>inception-v3-tf</td>
     <td>ImageNet</td>
     <td>acc@top-1</td>
     <td>0.03</td>
@@ -281,7 +260,7 @@ The following table shows the absolute accuracy drop that is calculated as the d
     <td>0.01</td>
   </tr>
   <tr>
-    <td>mobilenet-<br>ssd-CF</td>
+    <td>mobilenet-<br>ssd-caffe</td>
     <td>VOC2012</td>
     <td>mAP</td>
     <td>0.35</td>
@@ -289,15 +268,15 @@ The following table shows the absolute accuracy drop that is calculated as the d
     <td>0.34</td>
   </tr>
   <tr>
-    <td>mobilenet-v1-1.0-<br>224-TF</td>
+    <td>mobilenet-v1-1.0-<br>224-caffe</td>
     <td>ImageNet</td>
     <td>acc@top-1</td>
-    <td>0.27</td>
-    <td>0.20</td>
-    <td>0.20</td>
+    <td>0.19</td>
+    <td>0.18</td>
+    <td>0.18</td>
   </tr>
   <tr>
-    <td>mobilenet-v2-1.0-<br>224-TF</td>
+    <td>mobilenet-v2-1.0-<br>224-tf</td>
     <td>ImageNet</td>
     <td>acc@top-1</td>
     <td>0.45</td>
@@ -305,12 +284,20 @@ The following table shows the absolute accuracy drop that is calculated as the d
     <td>0.94</td>
   </tr>
   <tr>
-    <td>mobilenet-v2-<br>PYTORCH</td>
+    <td>mobilenet-v2-<br>caffe</td>
     <td>ImageNet</td>
     <td>acc@top-1</td>
-    <td>0.35</td>
-    <td>0.63</td>
-    <td>0.63</td>
+    <td>0.24</td>
+    <td>1.45</td>
+    <td>1.45</td>
+  </tr>
+  <tr>
+    <td>resnet-101-<br>caffe</td>
+    <td>ImageNet</td>
+    <td>acc@top-1</td>
+    <td>0.00</td>
+    <td>0.02</td>
+    <td>0.02</td>
   </tr>
   <tr>
     <td>resnet-18-<br>pytorch</td>
@@ -321,60 +308,52 @@ The following table shows the absolute accuracy drop that is calculated as the d
     <td>0.25</td>
   </tr>
   <tr>
-    <td>resnet-50-<br>PYTORCH</td>
+    <td>resnet-50-<br>caffe</td>
     <td>ImageNet</td>
     <td>acc@top-1</td>
-    <td>0.18</td>
-    <td>0.19</td>
-    <td>0.19</td>
+    <td>0.16</td>
+    <td>0.12</td>
+    <td>0.12</td>
   </tr>
   <tr>
-    <td>resnet-50-<br>TF</td>
+    <td>resnet-50-<br>pytorch</td>
     <td>ImageNet</td>
     <td>acc@top-1</td>
-    <td>0.15</td>
-    <td>0.15</td>
-    <td>0.10</td>
+    <td>0.20</td>
+    <td>0.17</td>
+    <td>0.17</td>
   </tr>
   <tr>
-    <td>squeezenet1.1-<br>CF</td>
+    <td>squeezenet1.1-<br>caffe</td>
     <td>ImageNet</td>
     <td>acc@top-1</td>
     <td>0.66</td>
-    <td>0.66</td>
+    <td>0.64</td>
     <td>0.64</td>
   </tr>
   <tr>
     <td>ssd_mobilenet_<br>v1_coco-tf</td>
-    <td>VOC2012</td>
+    <td>MS COCO</td>
     <td>COCO mAp</td>
     <td>0.24</td>
-    <td>0.24</td>
+    <td>3.07</td>
     <td>3.07</td>
   </tr>
   <tr>
-    <td>ssd300-CF</td>
+    <td>ssd300-caffe</td>
     <td>MS COCO</td>
     <td>COCO mAp</td>
     <td>0.06</td>
-    <td>0.06</td>
+    <td>0.05</td>
     <td>0.05</td>
   </tr>
   <tr>
-    <td>ssdlite_<br>mobilenet_<br>v2-TF</td>
+    <td>ssdlite_<br>mobilenet_<br>v2-tf</td>
     <td>MS COCO</td>
     <td>COCO mAp</td>
     <td>0.14</td>
-    <td>0.14</td>
     <td>0.47</td>
-  </tr>
-  <tr>
-    <td>yolo_v3-TF</td>
-    <td>MS COCO</td>
-    <td>COCO mAp</td>
-    <td>0.20</td>
-    <td>0.20</td>
-    <td>0.36</td>
+    <td>0.47</td>
   </tr>
 </table>
 

docs/documentation_guidelines.md

@@ -1,207 +0,0 @@
-# Documentation Guidelines
-
-## Authoring Guidelines
-
-We want our documentation to be easy to read and understand. This section describes guidelines for writing documentation that is clear, concise, confident, and courteous.
-
-For details on organizing content and how we use Markdown, refer to [Structure and Formatting](#structure-and-formatting-guidelines).
-
-###	Use Simple English
-
-* **Be brief.** Use short sentences and paragraphs. Stick to the principle of one main idea per sentence, plus one additional point if needed. Each paragraph should address one main idea. Remember the basic structure of a paragraph: Introduction, body, and conclusion.
-* **Use simple words.** Use simple words to increase reader comprehension and reduce ambiguity. Follow our tips for making good word choices:
-   * **Avoid jargon:** Write for your audience, using everyday language where possible, and technical terms where appropriate. Avoid clichés, idioms, and metaphors.
-   * **Be consistent:** Use one term for each concept or action and use it consistently.
-   * **Avoid “fancy” words and phrases:** If there is a simpler word or phrase, use it.
-
-###	Make Content Scannable
-
-Organize your content to make it scannable for the reader, which helps them find what they need quickly, and to understand the information more efficiently.
-
-* **Put the most important content first.** Make sure your introduction clearly communicates what the reader can find on the page. Present the point of the document first, and organize supporting information towards the end of the page.
-* **Write scannable headings.** Expect readers of documentation to skim and scan the content, and to leave if they don’t find what they need quickly. Good headings add organization to your content and help the reader to find and understand content more effectively. Follow our guidelines for writing effective Headings.
-* **Write great link text.** Great link text tells the reader what they can expect when they click on a link. It also helps make the page more scannable. Follow our guidelines for writing Link text.
-
-###	Use Strong Verbs
-
-* **Passive verbs make writing stuffy and formal**. Use strong verbs to get to the point and avoid unnecessary words and phrases.
-* **Commands**, also called imperatives, are the fastest and most direct way of giving someone instructions.
-* **Use simple present tense** instead of future tense for most text. Search for the words “will” or “shall” to find future tense instances. Future tense is acceptable for conditional statements, such as in a caution or a warning.
-
-###	Parallelism
-
-Parallelism refers to the practice of using similar patterns of grammar, and sometimes length, to coordinate words, phrases, and clauses.  
-Use parallel construction in lists. The table below shows some unparallel structures and how they can be made parallel with a little rewording.
-
-| Parallel (do)           | Unparallel (don’t)        |
-|-------------------------|---------------------------|
-| 1. Mount the panel.     | 1. Mount the panel.       |
-| 2. Install the battery. | 2. Battery installation.  |
-| 3. Wire the keypad.     | 3. Wiring the keypad.     |
-
-## Structure Guidelines
-
-Content should be organized to support scanning. Consistent organization, formatting, and writing style helps readers quickly find what they need and to understand the content more effectively. This section describes our organization.
-
-### Website Structure
-The documentation website is organized this way:
-
-1. **Getting Started**: installation guides, prerequisites, how to set working environment
-2. **How To-s**: videos and guides, for some reason not included in **Guides** section
-3. **Guides**: developer guides for all OpenVINO Toolkit components
-4. **Resources**: samples, demos, pre-trained models and tools (Accuracy Checker, Post-Training Optimization etc)
-5. **Performance Information**: benchmark tests information, ways to improve Performance
-6. **API References**: in-built wiki with class structures
-
-### Page Structures
-Each page in the documentation should follow the basic format of:
-
-* **Overview**: 1-2 sentences describing what this page shows and why it matters
-* **Prerequisites**: Describe any pre-work necessary to the content (if appropriate)
-* **Content**
-* **Next steps**: List links to next steps (if appropriate)
-* **Related topics**: List links to related content (if appropriate)
-
-## Formatting Guidelines
-We apply the formatting using Markdown.
-
-### Inline Text Formatting
-Use our quick reference for the most commonly used inline text elements:
-
-| Element                 | Markdown Examples            |   Notes |
-|---------------          |---------------------| -------- |
-| Notes                   | `> **NOTE**: text`   | The space between `>` and `**NOTE**` is required  |
-| Code elements (variables, methods, classes)      | ``` `text` ```      |              |
-| Code snippets           | ` ```cpp`<br>text<br>` ``` `      |                         |
-| Console commands, output| ` ```sh`<br>text<br>` ``` ` |                               |
-| Product name	          | OpenVINO™,<br>Intel® Distribution of OpenVINO™ toolkit  |   |
-
-### Headings
-Use headings to section and organize your content for better readability and clarity.
-
-* **Use strong verbs.** Strong, active verbs get to the point. Avoid **-ing** verbs, such as Running, Testing, etc.
-* **Be concise and descriptive.** Use only the words necessary to describe the section.
-* **Use sentence case.** Capitalize first letter every word except articles, prepositions: **Build Inference Engine on Linux**
-* **Avoid punctuation.** Unless your heading is a question, don’t use sentence punctuation in headings.
-* **Use parallel structure.** Headings at the same level should use the same grammatical pattern. This provides structure to the document and helps users find information more easily. See [Parallelism](#parallelism).
-
-Use following rules to write headings in your documentation:
-* All files must have a top level heading, which is the title for the page.
-  ```
-  # First Level Heading
-  ```
-* Up to three additional levels of headings are allowed under the title heading.
-  ```
-  ## Second Level Heading
-  ### Third Level Heading
-  #### Fourth Level Heading
-  ```
-* Each heading should be followed by at least one paragraph of content. Avoid two or more consecutive headings.
-* To add a link to a heading in the current page, do the following:  
-    * Add the `<a>` HTML tag and set a unique name to your heading with `name` argument:
-    ```XML
-    ## <a name="set-the-environment-variables"></a> Set the Environment Variables
-    ```
-    * Place the link to the heading using the set name:
-    ```XML
-        Refer <a href="#set-the-environment-variables">here</a>
-    ```
-
-### Code Snippets and Examples
-You can create fenced code snippets by placing triple backticks ` ``` `  before and after the code block and add an optional language identifier to enable syntax highlighting in your fenced code block. For example, to syntax highlight C++ code:
-```
-```cpp
-#include <inference_engine.hpp>
-void main(int argc, char *argv[])
-{
-    InferenceEngine::Core ie;
-}
-```
-
-```cpp
-#include <inference_engine.hpp>
-void main(int argc, char *argv[])
-{
-    InferenceEngine::Core ie;
-}
-```
-
-For full list with supported languages refer [here](https://github.com/github/linguist/blob/master/lib/linguist/languages.yml).
-
-### Lists and Instructions
-This section provides information about formatting numbered and bulleted lists.
-
-#### Numbered Lists
-Use a numbered list when the order or priority of the items is important, such as step-by-step instructions:
-```
-1. **Initialize core** to..
-2. **Prepare input** for..
-3. ...
-```
-Numbered lists are most frequently used for procedures. Use numbered lists to show sequence for the items. Follow our guidelines for numbered lists:
-* Make few first words describe whole step and bold it:  
-  6) **Prepare input**. You can use one of the following options to prepare input
-* Introduce a numbered list with a sentence. End the setup text with a colon. Example:
-  "To configure the unit, perform the following steps:"
-* Each item in the list should be [parallel](#parallelism).
-* Treat numbered list items as full sentences with correct ending punctuation.
-* You may interrupt numbered lists with other content, if relevant, e.g. explanatory text, commands, or code.
-
-#### Bulleted lists
-Use a bulleted list when the order of the items is not important:
-```
-* Option X
-* Option Y
-* Option Z
-```
-* Introduce a bulleted list with a sentence. End the setup text with a colon. Example:  
-  “To repair the unit, you will need the following items:”
-* Make few first words describe whole step and bold it:  
-  * **Prepare input**. You can use one of the following options to prepare input
-* Each item in the list should be parallel.
-* Avoid interrupting bulleted lists with other paragraph styles.
-* Second-level bullets are acceptable; avoid third-level bullets.
-
-For both list types, keep all items in the list parallel. See [Parallelism](#parallelism):
-
-### Links
-All links in content should follow these guidelines:
-
-* **Avoid generic text:** Don’t use generic, uninformative link text such as “click here” or “read more”.
-* **Write descriptive link text:** Link text should describe where the link goes, without having to read the surrounding text.
-* **Use unique link text:** Each link text on a page should be unique. If users see the same link text twice on a page, they’ll assume it goes to the same place.
-* **Start link text with keywords:** Frontload the link text with the most important words to help users scan the text.
-
-Use following examples to write links in your documentation:
-* To add a cross-reference to another documentation page, use file path from [DoxygenLayout.xml](./openvino-documentation/blob/master/docs/doxygen/DoxygenLayout.xml) (don't use direct links to docs.openvinotoolkit.org):
-```
-[OpenVINO Installation on Linux](./docs/install_guides/installing-openvino-linux.md)
-```
-Currently links to headings in other markdown files are not supported. To refer to a specific section, you may use the following example:  
-```
-"Use **Set Environment Variables** section in the [OpenVINO Installation on Linux](./docs/install_guides/installing-openvino-linux.md) document.
-```
-* To add URL link to any software, link to the latest version, for example:
-```
-For more details, see [CMake page](https://cmake.org/cmake/help/latest/manual/cmake.1.html#manual:cmake(1))
-```
-
-## Graphics Guidelines
-Use images or figures to convey information that may be difficult to explain using words alone. Well-planned graphics reduce the amount of text required to explain a topic or example.
-
-Follow these guidelines when using graphics in support of your documentation:
-
-* Keep it simple. Use images that serve a specific purpose in your document, and contain only the information the reader needs.
-* Avoid graphics that will need frequent updating. Don’t include information in a graphic that might change with each release, such as product versions.
-* Use either PNG or JPEG bitmap files for screenshots and SVG files for vector graphics.
-* Place the image immediately after the text it helps clarify, or as close as possible.
-* Use the Markdown directives to insert images and figures into the document. Include both alt text, and the title.
-* Include at least one direct reference to an image from the main text, using the figure number.
-
-Images should follow these naming and location conventions:
-
-* Save the image files in a figures folder at the same level as the file that will reference the image.
-* Name image files according to the following rules:
-   * Use only lower case letters.
-   * Separate multiple words in filenames using dashes.
-   * Name images using the filename of the file they appear on and add a number to indicate their place in the file. For example, the third figure added to the `welcome.md` file must be named `welcome-3.png`.