Feature/azaytsev/docs 2021 1

build-instruction.md

@@ -46,9 +46,6 @@ 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:

docs/IE_DG/Deep_Learning_Inference_Engine_DevGuide.md

@@ -76,7 +76,6 @@ 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)

docs/IE_DG/Glossary.md

@@ -64,7 +64,7 @@ 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, FPGA, etc.) |
+| Device (Affinitity) | A preferred Intel(R) hardware device to run the inference (CPU, GPU, 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 |

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 or FPGA, the Inference Engine uses the asynchronous approach (see
+To gain better performance on accelerators, such as VPU, 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/Samples_Overview.md

@@ -49,9 +49,11 @@ You can download the [pre-trained models](@ref omz_models_intel_index) using the
 
 The officially supported Linux* build environment is the following:
 
-* 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
+* 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/blob/master/build-instruction.md).
 
 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
@@ -99,7 +101,7 @@ 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 2.8.12 or higher
+* CMake* version 3.10 or higher
 
 > **NOTE**: If you want to use Microsoft Visual Studio 2019, you are required to install CMake 3.14.
 

docs/IE_DG/inference_engine_intro.md

@@ -3,7 +3,7 @@ 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, 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.
+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.
 
 For Intel® Distribution of OpenVINO™ toolkit, Inference Engine binaries are delivered within release packages. 
 
@@ -13,7 +13,7 @@ To learn about how to use the Inference Engine API for your application, see the
 
 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® hardware device: CPU, GPU, VPU, FPGA, 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® hardware device: CPU, GPU, VPU, etc. Each plugin implements the unified API and provides additional hardware-specific APIs.
 
 Modules in the Inference Engine component
 ---------------------------------------
@@ -53,7 +53,6 @@ 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® 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
 |HETERO|Automatic splitting of a network inference between several devices (for example if a device doesn't support certain layers|
@@ -65,7 +64,6 @@ 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/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* 16.04/CentOS* 7.4/MS Windows* 10 |
-| Target            | Ubuntu* 16.04/CentOS* 7.4/MS Windows* 10 |
+| Development       | Ubuntu* 18.04, CentOS* 7.5, MS Windows* 10 |
+| Target            | Ubuntu* 18.04, CentOS* 7.5, 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).