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part2_aws_sdaccel.md

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Clone hls4ml wrapper for SDAccel

git clone https://github.com/FPGA4HEP/hls4ml_c.git
cd hls4ml_c
git pull #to fetch the latest changes

Edit Makefile in the hls4ml_c directory to change default input directory name:

HLS4ML_PROJECT := my-hls-test-FAVOURITE-MODEL

Check out SDAccel and setup environment

git clone https://github.com/aws/aws-fpga.git $AWS_FPGA_REPO_DIR

Every time you login do:

cd $AWS_FPGA_REPO_DIR                                         
source sdaccel_setup.sh

or today you can also just follow this shorcut we have set up for you:

cd ~/
source setup_sdaccel.sh

NB: if you had hls4ml activated, you should log out/in first.

More detailed information here

Run software simulation, hardware emulation and build FPGA binary

make clean                                                                 
make check TARGETS=sw_emu DEVICES=$AWS_PLATFORM all                 #software emulation
make check TARGETS=hw_emu DEVICES=$AWS_PLATFORM all                 #hardware emulation
make TARGETS=hw DEVICES=$AWS_PLATFORM all && ./create.sh            #firmware building

Run on real FPGA

Launch a F1 instance and copy the host and binary files (.awsxclbin) from the T2 (nb, first copy to your laptop).

Setup the SDAccel environment on the F1 as well:

git clone https://github.com/aws/aws-fpga.git $AWS_FPGA_REPO_DIR
cd $AWS_FPGA_REPO_DIR 
source sdaccel_setup.sh
sudo sh
export VIVADO_TOOL_VERSION=2018.2
curl -s https://s3.amazonaws.com/aws-fpga-developer-ami/1.5.0/Patches/xrt_201802.2.1.0_7.5.1804-xrt.rpm -o xrt_201802.2.1.0_7.5.1804-xrt.rpm
curl -s https://s3.amazonaws.com/aws-fpga-developer-ami/1.5.0/Patches/xrt_201802.2.1.0_7.5.1804-aws.rpm -o xrt_201802.2.1.0_7.5.1804-aws.rpm
sudo yum reinstall -y xrt_*-xrt.rpm
sudo yum install -y xrt_*-aws.rpm
source /home/centos/src/project_data/aws-fpga/sdaccel_runtime_setup.sh

But today you can also just run these two scripts:

source setup_sdaccel_fpga_base.sh
source setup_sdaccel_fpga.sh

Now copy the input features and keras prediction files from your hls4ml project directory on the T2 (my-hls-test-FAVOURITE-MODEL/tb_data/) to the F1 to pass it to the FPGA.

Finally, you can accelerate your NN inference on the FPGA running on the input features:

./host N data_dir

where N is number of batches of 32 events (suggest use N=6168 if use the provided input features list), and data_dir is the directory with input features and keras predictions files.

The application will produce a file with the predictions from the FPGA run. Compare it with HLS and Keras calculations using the extract_roc.py script in the hls4ml directory on the T2 instance (nb, copy the tb_output_data.dat from the F1 to hls4ml/keras-to-hls directory on the T2)

python extract_roc.py -c keras-config-FAVOURITE-MODEL.yml -f tb_output_data.dat