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_hints.md

@@ -12,7 +12,7 @@ Also, while the resulting performance may be optimal for the specific combinatio
 Beyond execution _parameters_ there are potentially many device-specific details like _scheduling_ that greatly affect the performance. 
 Specifically, GPU-oriented tricks like batching, which combines many (potentially tens) of input images to achieve optimal throughput, do not always map well to the CPU, as e.g. detailed in the next sections.
 The hints allow to really hide _execution_ specifics required to saturate the device. For example, no need to explicitly combine multiple inputs into a batch to achieve good GPU performance.
-Instead, it is possible to keep a separate infer request per camera or another source of input and process the requests in parallel using <a href="#ov-async-api">OpenVINO Async API</a>.
+Instead, it is possible to keep a separate infer request per camera or another source of input and process the requests in parallel using Async API as explained in the [common-optimizations section](@ref openvino_docs_deployment_optimization_guide_common).
 
 The only requirement for the application to leverage the throughput is about **running multiple inference requests in parallel**.
 OpenVINO's device-specific implementation of the hints will take care of the rest. This allows a developer to greatly simplify the app-logic.