The Intel® Distribution of OpenVINO™ toolkit quickly deploys applications and solutions that emulate human vision. Based on Convolutional Neural Networks (CNN), the toolkit extends computer vision (CV) workloads across Intel® hardware, maximizing performance. The Intel® Distribution of OpenVINO™ toolkit includes the Intel® Deep Learning Deployment Toolkit (Intel® DLDT).
The Intel® Distribution of OpenVINO™ toolkit for Linux* with FPGA Support:
Included with the Installation and installed by default:
|Model Optimizer||This tool imports, converts, and optimizes models that were trained in popular frameworks to a format usable by Intel tools, especially the Inference Engine. |
Popular frameworks include Caffe*, TensorFlow*, MXNet*, and ONNX*.
|Inference Engine||This is the engine that runs the deep learning model. It includes a set of libraries for an easy inference integration into your applications.|
|Drivers and runtimes for OpenCL™ version 2.1||Enables OpenCL on the GPU/CPU for Intel® processors|
|Intel® Media SDK||Offers access to hardware accelerated video codecs and frame processing|
|Pre-compiled FPGA bitstream samples||Pre-compiled bitstream samples for the Intel® Programmable Acceleration Card with Intel® Arria® 10 GX FPGA, and Intel® Vision Accelerator Design with an Intel® Arria 10 FPGA SG2.|
|Intel® FPGA SDK for OpenCL™ software technology||The Intel® FPGA RTE for OpenCL™ provides utilities, host runtime libraries, drivers, and RTE-specific libraries and files|
|OpenCV||OpenCV* community version compiled for Intel® hardware|
|Inference Engine Code Samples||A set of simple console applications demonstrating how to utilize specific OpenVINO capabilities in an application and how to perform specific tasks, such as loading a model, running inference, querying specific device capabilities, and more.|
|Demo Applications||A set of simple console applications that provide robust application templates to help you implement specific deep learning scenarios.|
The development and target platforms have the same requirements, but you can select different components during the installation, based on your intended use.
NOTE: Intel® Arria 10 FPGA (Mustang-F100-A10) SG1 is no longer supported. If you use Intel® Vision Accelerator Design with an Intel® Arria 10 FPGA (Mustang-F100-A10) Speed Grade 1, we recommend continuing to use the Intel® Distribution of OpenVINO™ toolkit 2020.1 release.
NOTE: Intel® Arria® 10 GX FPGA Development Kit is no longer supported. For the Intel® Arria® 10 FPGA GX Development Kit configuration guide, refer to the 2019 R1.1 documentation.
This guide provides step-by-step instructions on how to install the Intel® Distribution of OpenVINO™ toolkit with FPGA Support. Links are provided for each type of compatible hardware including downloads, initialization and configuration steps. The following steps will be covered:
Download the Intel® Distribution of OpenVINO™ toolkit package file from Intel® Distribution of OpenVINO™ toolkit for Linux* with FPGA Support. Select the Intel® Distribution of OpenVINO™ toolkit for Linux with FPGA Support package from the dropdown menu.
The first core components are installed. Continue to the next section to install additional dependencies.
These dependencies are required for:
The dependencies are installed. Continue to the next section to configure the Model Optimizer.
The Model Optimizer is a Python*-based command line tool for importing trained models from popular deep learning frameworks such as Caffe*, TensorFlow*, Apache MXNet*, ONNX* and Kaldi*.
The Model Optimizer is a key component of the Intel Distribution of OpenVINO toolkit. You cannot perform inference on your trained model without running the model through the Model Optimizer. When you run a pre-trained model through the Model Optimizer, your output is an Intermediate Representation (IR) of the network. The Intermediate Representation is a pair of files that describe the whole model:
.xml: Describes the network topology
.bin: Contains the weights and biases binary data
For more information about the Model Optimizer, refer to the Model Optimizer Developer Guide.
IMPORTANT: The Internet access is required to execute the following steps successfully. If you have access to the Internet through the proxy server only, please make sure that it is configured in your environment.
You can choose to either configure all supported frameworks at once OR configure one framework at a time. Choose the option that best suits your needs. If you see error messages, make sure you installed all dependencies.
NOTE: If you installed the Intel® Distribution of OpenVINO™ to the non-default install directory, replace
/opt/intelwith the directory in which you installed the software.
Option 1: Configure all supported frameworks at the same time
Option 2: Configure each framework separately
Configure individual frameworks separately ONLY if you did not select Option 1 above.
You are ready to compile the samples by running the verification scripts.
To verify the installation and compile two samples, run the verification applications provided with the product on the CPU:
car.pngimage in the demo directory. When the verification script completes, you will have the label and confidence for the top-10 categories:
Run the Inference Pipeline verification script:
This verification script builds the Security Barrier Camera Demo application included in the package.
This verification script uses the
car_1.bmp image in the demo directory to show an inference pipeline using three of the pre-trained models. The verification script uses vehicle recognition in which vehicle attributes build on each other to narrow in on a specific attribute.
First, an object is identified as a vehicle. This identification is used as input to the next model, which identifies specific vehicle attributes, including the license plate. Finally, the attributes identified as the license plate are used as input to the third model, which recognizes specific characters in the license plate.
When the verification script completes, you will see an image that displays the resulting frame with detections rendered as bounding boxes, and text:
To learn about the verification scripts, see the
README.txt file in
For a description of the Intel Distribution of OpenVINO™ pre-trained object detection and object recognition models, see Overview of OpenVINO™ Toolkit Pre-Trained Models.
You have completed all required installation, configuration and build steps in this guide to use your CPU to work with your trained models. To use other hardware, see Install and Configure your Compatible Hardware below.
Install your compatible hardware from the list of supported components below.
NOTE: Once you've completed your hardware installation, you'll return to this guide to finish installation and configuration of the Intel® Distribution of OpenVINO™ toolkit.
Links to install and configure compatible hardware
Congratulations, you have finished the Intel® Distribution of OpenVINO™ toolkit installation for FPGA. To learn more about how the Intel® Distribution of OpenVINO™ toolkit works, the Hello World tutorial and other resources are provided below.
Refer to the OpenVINO™ with FPGA Hello World Face Detection Exercise.
To learn more about converting models, go to: