DOCS: ported changes from 2022.1 release branch (#11206)

* Extensibility guide with FE extensions and remove OV_FRAMEWORK_MAP from docs

* Rework of Extensibility Intro, adopted examples to missing OPENVINO_FRAMEWORK_MAP

* Removed OPENVINO_FRAMEWORK_MAP reference

* Frontend extension detailed documentation

* Fixed distributed snippets

* Fixed snippet inclusion in FE extension document and chapter headers

* Fixed wrong name in a snippet reference

* Fixed test for template extension due to changed number of loaded extensions

* Update docs/Extensibility_UG/frontend_extensions.md

Co-authored-by: Ivan Tikhonov <ivan.tikhonov@intel.com>

* Minor fixes in extension snippets

* Small grammar fix

Co-authored-by: Ivan Tikhonov <ivan.tikhonov@intel.com>

Co-authored-by: Ivan Tikhonov <ivan.tikhonov@intel.com>

* DOCS: transition banner (#10973)

* transition banner

* minor fix

* update transition banner

* updates

* update custom.js

* updates

* updates

* Documentation fixes (#11044)

* Benchmark app usage

* Fixed link to the devices

* More fixes

* Update docs/OV_Runtime_UG/multi_device.md

Co-authored-by: Sergey Lyubimtsev <sergey.lyubimtsev@intel.com>

* Removed several hardcoded links

Co-authored-by: Sergey Lyubimtsev <sergey.lyubimtsev@intel.com>

* Updated documentation for compile_tool (#11049)

* Added deployment guide (#11060)

* Added deployment guide

* Added local distribution

* Updates

* Fixed more indentations

* Removed obsolete code snippets (#11061)

* Removed obsolete code snippets

* NCC style

* Fixed NCC for BA

* Add a troubleshooting issue for PRC installation (#11074)

* updates

* adding gna to linux

* add missing reference

* update

* Update docs/install_guides/installing-model-dev-tools.md

Co-authored-by: Sergey Lyubimtsev <sergey.lyubimtsev@intel.com>

* Update docs/install_guides/installing-model-dev-tools.md

Co-authored-by: Sergey Lyubimtsev <sergey.lyubimtsev@intel.com>

* Update docs/install_guides/installing-model-dev-tools.md

Co-authored-by: Sergey Lyubimtsev <sergey.lyubimtsev@intel.com>

* Update docs/install_guides/installing-model-dev-tools.md

Co-authored-by: Sergey Lyubimtsev <sergey.lyubimtsev@intel.com>

* Update docs/install_guides/installing-model-dev-tools.md

Co-authored-by: Sergey Lyubimtsev <sergey.lyubimtsev@intel.com>

* update

* minor updates

* add gna item to yum and apt

* add gna to get started page

* update reference formatting

* merge commit

* add a troubleshooting issue

* update

* update

* fix CVS-71846

Co-authored-by: Sergey Lyubimtsev <sergey.lyubimtsev@intel.com>

* DOCS: fixed hardcoded links  (#11100)

* Fixes

* Use links

* applying reviewers comments to the Opt Guide (#11093)

* applying reviewrs comments

* fixed refs, more structuring (bold, bullets, etc)

* refactoring tput/latency sections

* next iteration (mostly latency), also brushed the auto-batching and other sections

* updates sync/async images

* common opts brushed

* WIP tput redesigned

* minor brushing of common and auto-batching

* Tput fully refactored

* fixed doc name in the link

* moved int8 perf counters to the right section

* fixed links

* fixed broken quotes

* fixed more links

* add ref to the internals to the TOC

* Added a note on the batch size

Co-authored-by: Andrey Zaytsev <andrey.zaytsev@intel.com>

* [80085] New images for docs (#11114)

* change doc structure

* fix manager tools

* fix manager tools 3 step

* fix manager tools 3 step

* new img

* new img for OV Runtime

* fix steps

* steps

* fix intendents

* change list

* fix space

* fix space

* code snippets fix

* change display

* Benchmarks 2022 1 (#11130)

* Minor fixes

* Updates for 2022.1

* Edits according to the review

* Edits according to review comments

* Edits according to review comments

* Edits according to review comments

* Fixed table

* Edits according to review comments

* Removed config for Intel® Core™ i7-11850HE

* Removed forward-tacotron-duration-prediction-241 graph

* Added resnet-18-pytorch

* Add info about Docker images in Deployment guide (#11136)

* Renamed user guides (#11137)

* fix screenshot (#11140)

* More conservative recommendations on dynamic shapes usage in docs (#11161)

* More conservative recommendations about using dynamic shapes

* Duplicated statement from C++ part to Python part of reshape doc (no semantical changes)

* Update ShapeInference.md (#11168)

* Benchmarks 2022 1 updates (#11180)

* Updated graphs

* Quick fix for TODO in Dynamic Shapes article

* Anchor link fixes

* Fixed DM config (#11199)

* DOCS: doxy sphinxtabs (#11027)

* initial implementation of doxy sphinxtabs

* fixes

* fixes

* fixes

* fixes

* fixes

* WA for ignored visibility attribute

* Fixes

Co-authored-by: Sergey Lyalin <sergey.lyalin@intel.com>
Co-authored-by: Ivan Tikhonov <ivan.tikhonov@intel.com>
Co-authored-by: Nikolay Tyukaev <nikolay.tyukaev@intel.com>
Co-authored-by: Sergey Lyubimtsev <sergey.lyubimtsev@intel.com>
Co-authored-by: Yuan Xu <yuan1.xu@intel.com>
Co-authored-by: Maxim Shevtsov <maxim.y.shevtsov@intel.com>
Co-authored-by: Andrey Zaytsev <andrey.zaytsev@intel.com>
Co-authored-by: Tatiana Savina <tatiana.savina@intel.com>
Co-authored-by: Ilya Naumov <ilya.naumov@intel.com>
Co-authored-by: Evgenya Stepyreva <evgenya.stepyreva@intel.com>

docs/optimization_guide/dldt_deployment_optimization_latency.md

@@ -5,25 +5,26 @@
 .. toctree::
    :maxdepth: 1
    :hidden:
-    
+
    openvino_docs_IE_DG_Model_caching_overview
 
 @endsphinxdirective
 
 ## Latency Specifics
 A significant fraction of applications focused on the situations where typically a single model is loaded (and single input is used) at a time.
-This is a regular "consumer" use case and a default (also for the legacy reasons) performance setup for any OpenVINO device.
-Notice that an application can  create more than one request if needed (for example to support asynchronous inputs population), the question is really about how many requests are being executed in parallel.
+This is a regular "consumer" use case.
+While an application can create more than one request if needed (for example to support [asynchronous inputs population](./dldt_deployment_optimization_common.md)), the inference performance depends on **how many requests are being inferenced in parallel** on a device.
 
 Similarly, when multiple models are served on the same device, it is important whether the models are executed simultaneously, or in chain (for example in the inference pipeline).
-As expected, the lowest latency is achieved with only one concurrent inference at a moment. Accordingly, any additional concurrency usually results in the latency growing fast.
+As expected, the easiest way to achieve the lowest latency is **running only one concurrent inference at a moment** on the device. Accordingly, any additional concurrency usually results in the latency growing fast.
 
-However, for example, specific configurations, like multi-socket CPUs can deliver as high number of requests (at the same minimal latency) as there are NUMA nodes in the machine.
-Thus, human expertise is required to get the most out of the device even in the latency case. Consider using [OpenVINO high-level performance hints](../OV_Runtime_UG/performance_hints.md) instead.
+However, some conventional "root" devices (e.g. CPU or GPU) can be in fact internally composed of several "sub-devices".  In many cases letting the OpenVINO to transparently leverage the "sub-devices" helps to improve the application throughput (e.g. serve multiple clients simultaneously) without degrading the latency.  For example, multi-socket CPUs can deliver as high number of requests (at the same minimal latency) as there are NUMA nodes in the machine. Similarly, a multi-tile GPU (which is essentially multiple GPUs in a single package), can deliver a multi-tile scalability with the number of inference requests, while preserving the single-tile latency.
 
-**NOTE**: [OpenVINO performance hints](./dldt_deployment_optimization_hints.md) is a recommended way for performance configuration, which is both device-agnostic and future-proof. 
+Thus, human expertise is required to get more _throughput_ out of the device even in the inherently latency-oriented cases. OpenVINO can take this configuration burden via [high-level performance hints](../OV_Runtime_UG/performance_hints.md).
 
-In the case when there are multiple models to be used simultaneously, consider using different devices for inferencing the different models. Finally, when multiple models are executed in parallel on the device, using additional `ov::hint::model_priority` may help to define relative priorities of the models (please refer to the documentation on the [matrix features support for OpenVINO devices](../OV_Runtime_UG/supported_plugins/Device_Plugins.md) to check for the support of the feature by the specific device).
+> **NOTE**: [OpenVINO performance hints](./dldt_deployment_optimization_hints.md) is a recommended way for performance configuration, which is both device-agnostic and future-proof.
+
+In the case when there are multiple models to be used simultaneously, consider using different devices for inferencing the different models. Finally, when multiple models are executed in parallel on the device, using additional `ov::hint::model_priority` may help to define relative priorities of the models (please refer to the documentation on the [matrix features support for OpenVINO devices](@ref features_support_matrix) to check for the support of the feature by the specific device).
 
 ## First-Inference Latency and Model Load/Compile Time
 There are cases when model loading/compilation are heavily contributing to the end-to-end latencies.