openvino/docs/install_guides/VisionAcceleratorFPGA_Configure_2019R1.md

Configuration Guide for the Intel® Distribution of OpenVINO™ toolkit 2019R1 and the Intel® Vision Accelerator Design with an Intel® Arria® 10 FPGA (IEI's Mustang-F100-A10) on Linux*

NOTES:

• For a first-time installation, use all steps.
• Use step 1 only after receiving a new FPGA card.
• Repeat steps 2-4 when installing a new version of the Intel® Distribution of OpenVINO™ toolkit.
• Use steps 3-4 when a Neural Network topology used by an Intel® Distribution of OpenVINO™ toolkit application changes.

1. Configure and Set Up the Intel® Vision Accelerator Design with an Intel® Arria® 10 FPGA

For the 2019R1.x releases, the Intel® Distribution of OpenVINO™ toolkit introduced a new board support package (BSP) a10_1150_sg1 for the Intel® Vision Accelerator Design with an Intel® Arria® 10 FPGA, which is included in the fpga_support_files.tgz archive below. To program the bitstreams for the Intel® Distribution of OpenVINO™ toolkit 2019R1.x, you need to program the BSP into the board using the USB blaster.

1. Download Intel® Quartus® Prime Programmer and Tools Standard Edition 18.1. Install the Intel® Quartus® Prime Programmer and Tools Software to the /home/<user>/intelFPGA/18.1 directory.

2. Download fpga_support_files.tgz from the Intel Registration Center to the ~/Downloads directory. The files in this .tgz archive are required to ensure your FPGA card and the Intel® Distribution of OpenVINO™ toolkit work correctly.

3. Go to the directory where you downloaded the fpga_support_files.tgz archive.

4. Unpack the .tgz file:

tar -xvzf fpga_support_files.tgz

A directory named fpga_support_files is created.

5. Go to the fpga_support_files directory:

cd fpga_support_files

6. Switch to superuser:

sudo su

7. Use the setup_env.sh script from fpga_support_files.tgz to set your environment variables:

source /home/<user>/Downloads/fpga_support_files/setup_env.sh

8. Uninstall any previous BSP before installing the OpenCL BSP for the 2019R1.x BSP:

aocl uninstall /opt/altera/aocl-pro-rte/aclrte-linux64/board/<BSP_package>/

9. Change directory to Downloads/fpga_support_files/:

cd /home/<user>/Downloads/fpga_support_files/

10. Run the FPGA dependencies script, which allows OpenCL to support Ubuntu* and recent kernels:

./install_openvino_fpga_dependencies.sh

11. When asked, select the appropriate hardware accelerators you plan to use so it installs the correct dependencies.

12. If you installed the 4.14 kernel as part of the installation script, you will need to reboot the machine and select the new kernel in the Ubuntu (grub) boot menu. You will also need to rerun setup_env.sh to set up your environmental variables again.

13. Export the Intel® Quartus® Prime Programmer environment variable:

export QUARTUS_ROOTDIR=/home/<user>/intelFPGA/18.1/qprogrammer

14. Set up the USB Blaster:

    1. Connect the cable between the board and the host system. Use the letter codes in the diagram below for the connection points:

    2. Connect the B end of the cable to point B on the board.

    3. Connect the F end of the cable to point F on the FPGA download cable.

    4. From point F end of the cable to point F on the FPGA download cable, the connection is as shown:

15. Run jtagconfig to ensure that your Intel FPGA Download Cable driver is ready to use:

jtagconfig

Your output is similar to:

1) USB-Blaster [1-6]
02E660DD   10AX115H1(.|E2|ES)/10AX115H2/.. 

16. Use jtagconfig to slow the clock. The message "No parameter named JtagClock" can be safely ignored.

jtagconfig --setparam 1 JtagClock 6M

17. (OPTIONAL) Confirm the clock is set to 6M:

jtagconfig --getparam 1 JtagClock

You should see the following:

6M

18. Go to /opt/altera/aocl-pro-rte/aclrte-linux64/board/a10_1150_sg1/bringup, where sg1_boardtest_2ddr_base.sofis located:

cd /opt/altera/aocl-pro-rte/aclrte-linux64/board/a10_1150_sg1/bringup

19. Program the new sof file to the board:

quartus_pgm -c 1 -m JTAG -o "p;sg1_boardtest_2ddr_base.sof"

20. Soft reboot:

sudo reboot

21. Open up a new terminal and restore sudo access and the environment variables:

sudo su
source /home/<user>/Downloads/fpga_support_files/setup_env.sh

22. Install OpenCL™ devices. Enter Y when prompted to install:

aocl install

23. Reboot the machine:

reboot

24. Open up a new terminal and restore sudo access and the environment variables:

sudo su
source /home/<user>/Downloads/fpga_support_files/setup_env.sh
export QUARTUS_ROOTDIR=/home/<user>/intelFPGA/18.1/qprogrammer

25. Run aocl diagnose:

aocl diagnose

Your screen displays DIAGNOSTIC_PASSED.

26. Use jtagconfig to slow the clock. The message "No parameter named JtagClock" can be safely ignored.

jtagconfig --setparam 1 JtagClock 6M

27. Go to /opt/intel/openvino/bitstreams/a10_vision_design_bitstreams/, where 2019R1_PL1_FP11_ResNet_SqueezeNet_VGG.aocx is located:

cd /opt/intel/openvino/bitstreams/a10_vision_design_bitstreams/

28. Program the 2019R1_PL1_FP11_ResNet_SqueezeNet_VGG.aocx file to the flash to be made permanently available even after power cycle:

aocl flash acl0 2019R1_PL1_FP11_ResNet_SqueezeNet_VGG.aocx

Note

: You will need the USB Blaster for this.

29. Hard reboot the host system including powering off.

30. Now Soft reboot the host system to ensure the new PCIe device is seen properly

reboot

31. Open up a new terminal and restore sudo access and the environment variables:

sudo su
source /home/<user>/Downloads/fpga_support_files/setup_env.sh

32. Check if the host system recognizes the Intel® Vision Accelerator Design with Intel® Arria® 10 FPGA board. Confirm you can detect the PCIe card:

lspci | grep -i Altera

Your output is similar to:

01:00.0 Processing accelerators: Altera Corporation Device 2494 (rev 01)

33. Run aocl diagnose:

aocl diagnose

You should see DIAGNOSTIC_PASSED before proceeding to the next steps.

2. Program a Bitstream

The bitstream you program should correspond to the topology you want to deploy. In this section, you program a SqueezeNet bitstream and deploy the classification sample with a SqueezeNet model that you used the Model Optimizer to convert in the steps before.

Important

: Only use bitstreams from the installed version of the Intel® Distribution of OpenVINO™ toolkit. Bitstreams from older versions of the Intel® Distribution of OpenVINO™ toolkit are incompatible with later versions of the Intel® Distribution of OpenVINO™ toolkit. For example, you cannot use the 1-0-1_A10DK_FP16_Generic bitstream, when the Intel® Distribution of OpenVINO™ toolkit supports the 2-0-1_A10DK_FP16_Generic bitstream.

Depending on how many bitstreams you selected, there are different folders for each FPGA card type which were downloaded in the Intel® Distribution of OpenVINO™ toolkit package:

1. For the Intel® Vision Accelerator Design with Intel® Arria® 10 FPGA the pre-trained bistreams are in /opt/intel/openvino/bitstreams/a10_vision_design_bitstreams. This example uses a SqueezeNet bitstream with low precision for the classification sample.

2. Rerun the environment setup script:

source /home/<user>/Downloads/fpga_support_files/setup_env.sh

3. Change to your home directory:

cd /home/<user>

4. Program the bitstream for the Intel® Vision Accelerator Design with Intel® Arria® 10 FPGA:

aocl program acl0 /opt/intel/openvino/bitstreams/a10_vision_design_bitstreams/2019R1_PL1_FP11_ResNet_SqueezeNet_VGG.aocx

Steps to Flash the FPGA Card

Note

:

• To avoid having to reprogram the board after a power down, a bitstream will be programmed to permanent memory on the Intel® Vision Accelerator Design with Intel® Arria® 10 FPGA. This will take about 20 minutes.
• The steps can be followed in the Configure and Setup the Intel® Vision Accelerator Design with an Intel® Arria® 10 FPGA section of this guide from steps 14-18 and 28-36.

3. Setup a Neural Network Model for FPGA

In this section, you will create an FP16 model suitable for hardware accelerators. For more information, see the FPGA plugin section in the Inference Engine Developer Guide.

1. Create a directory for the FP16 SqueezeNet Model:

mkdir /home/<user>/squeezenet1.1_FP16

2. Go to /home/<user>/squeezenet1.1_FP16:

cd /home/<user>/squeezenet1.1_FP16

3. Use the Model Optimizer to convert the FP32 SqueezeNet Caffe* model into an FP16 optimized Intermediate Representation (IR). The model files were downloaded when you ran the the Image Classification verification script while installing the Intel® Distribution of OpenVINO™ toolkit for Linux* with FPGA Support. To convert, run the Model Optimizer script with the following arguments:

python3 /opt/intel/openvino/deployment_tools/model_optimizer/mo.py --input_model /home/<user>/openvino_models/models/FP32/classification/squeezenet/1.1/caffe/squeezenet1.1.caffemodel --data_type FP16 --output_dir .

4. The squeezenet1.1.labels file contains the classes ImageNet uses. This file is included so that the inference results show text instead of classification numbers. Copy squeezenet1.1.labels to the your optimized model location:

cp /home/<user>/openvino_models/ir/FP32/classification/squeezenet/1.1/caffe/squeezenet1.1.labels  .

5. Copy a sample image to the release directory. You will use this with your optimized model:

sudo cp /opt/intel/openvino/deployment_tools/demo/car.png  ~/inference_engine_samples_build/intel64/Release

4. Run a Sample Application

1. Go to the samples directory

cd /home/<user>/inference_engine_samples_build/intel64/Release

2. Use an Inference Engine sample to run a sample application on the CPU:

./classification_sample_async -i car.png -m ~/openvino_models/ir/FP32/classification/squeezenet/1.1/caffe/squeezenet1.1.xml

Note the CPU throughput in Frames Per Second (FPS). This tells you how quickly the inference is done on the hardware. Now run the inference using the FPGA.

3. Add the -d option to target the FPGA:

./classification_sample_async -i car.png -m ~/squeezenet1.1_FP16/squeezenet1.1.xml -d HETERO:FPGA,CPU

The throughput on FPGA is listed and may show a lower FPS. This is due to the initialization time. To account for that, the next step increases the iterations to get a better sense of the speed the FPGA can run inference at.

4. Use -ni to increase the number of iterations, This option reduces the initialization impact:

./classification_sample_async -i car.png -m ~/squeezenet1.1_FP16/squeezenet1.1.xml -d HETERO:FPGA,CPU -ni 100

Congratulations, you are done with 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 are other resources are provided below.

Hello World Face Detection Tutorial

Use the Intel® Distribution of OpenVINO™ toolkit with FPGA Hello World Face Detection Exercise to learn more about how the software and hardware work together.

Additional Resources

Intel® Distribution of OpenVINO™ toolkit home page: https://software.intel.com/en-us/openvino-toolkit

Intel® Distribution of OpenVINO™ toolkit documentation: https://docs.openvinotoolkit.org/

Inference Engine FPGA plugin documentation: https://docs.openvinotoolkit.org/latest/_docs_IE_DG_supported_plugins_FPGA.html