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Feature/azaytsev/compile tool doc updates (#4237)

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Co-authored-by: Trawinski, Dariusz <dariusz.trawinski@intel.com>

* Updated openvino_docs.xml

* Updated Compile tool documentation, added extra description, removed FPGA related info

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Co-authored-by: Trawinski, Dariusz <dariusz.trawinski@intel.com>
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inference-engine/tools/compile_tool/README.md
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# Compile Tool {#openvino_inference_engine_tools_compile_tool_README}
The Compile tool is a C++ application that enables you to dump a loaded executable network blob.
The tool is delivered as an executable file that can be run on both Linux\* and Windows\*.
Compile tool is a C++ application that enables you to compile a network for inference on a specific device and export it to a binary file.
With the Compile Tool, you can compile a network using supported Inference Engine plugins on a machine that doesn't have the physical device connected and then transfer a generated file to any machine with the target inference device available.
The tool compiles networks for the following target devices using corresponding Inference Engine plugins:
* Intel® Neural Compute Stick 2 (MYRIAD plugin)
> **NOTE**: Intel® Distribution of OpenVINO™ toolkit no longer supports the Intel® Vision Accelerator Design with an Intel® Arria® 10 FPGA and the Intel® Programmable Acceleration Card with Intel® Arria® 10 GX FPGA. To compile a network for those devices, use the Compile Tool from the Intel® Distribution of OpenVINO™ toolkit [2020.3 LTS release](https://docs.openvinotoolkit.org/2020.3/_inference_engine_tools_compile_tool_README.html).
The tool is delivered as an executable file that can be run on both Linux* and Windows*.
The tool is located in the `<INSTALLROOT>/deployment_tools/tools/compile_tool` directory.
The workflow of the Compile tool is as follows:
@ -56,48 +65,24 @@ compile_tool [OPTIONS]
Value should be equal or greater than -1.
Overwrites value from config.
FPGA-specific options:
-DLA_ARCH_NAME <value> Optional. Specify architecture name used to compile executable network for FPGA device.
```
Running the application with the empty list of options yields an error message.
To dump a blob using a trained network, use the command below:
For example, to compile a blob for inference on an Intel® Neural Compute Stick 2 from a trained network, run the command below:
```sh
./compile_tool -m <path_to_model>/model_name.xml
./compile_tool -m <path_to_model>/model_name.xml -d MYRIAD
```
## FPGA Option
### Import a Compiled Blob File to Your Application
You can compile executable network without a connected FPGA device with a loaded DLA bitstream.
To do that, specify the architecture name of the DLA bitstream using the parameter `-DLA_ARCH_NAME`.
## Import and Export Functionality
### Export
To save a blob file from your application, call the `InferenceEngine::ExecutableNetwork::Export()`
method:
```cpp
InferenceEngine::ExecutableNetwork executableNetwork = core.LoadNetwork(network, "MYRIAD", {});
std::ofstream file{"model_name.blob"}
executableNetwork.Export(file);
```
### Import
To import a blob with the network into your application, call the
To import a blob with the network from a generated file into your application, use the
`InferenceEngine::Core::ImportNetwork` method:
Example:
```cpp
InferenceEngine::Core ie;
std::ifstream file{"model_name.blob"};
InferenceEngine::ExecutableNetwork = ie.ImportNetwork(file, "MYRIAD", {});
```
> **NOTE**: Prior to the import, models must be converted to the Inference Engine format
> (\*.xml + \*.bin) using the [Model Optimizer tool](https://software.intel.com/en-us/articles/OpenVINO-ModelOptimizer).