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part1_hls4ml_intro.md

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Download and install the package

git clone https://github.com/hls-fpga-machine-learning/hls4ml -b tutorial
cd hls4ml
pip install . --user
pip install enum --user

Run the tool (with your favourite model, e.g. 1-layer)

cd example-models
export FAVOURITEMODEL=1layer
hls4ml convert -c keras-config-${FAVOURITEMODEL}.yml

This will create a folder called my-hls-test-${FAVOURITEMODEL}. If you want to change the projectory directory name edit the yml configuration file.

Run project design synthesis with Vivado HLS

hls4ml build -p my-hls-test-${FAVOURITEMODEL} -c -s

or alternatively:

cd my-hls-test-${FAVOURITEMODEL}
vivado_hls -f build_prj.tcl "csim=1 synth=1 cosim=0 export=0"

Readout resource usage and latency from the synthesis report

./print-reports.sh -d ./

Extract and compare area under the ROC curve from keras (floating point calculations) and HLS (fixed point calculations)

pip install scikit-learn matplotlib==1.5.3 --user
python extract_roc.py -c keras-config-${FAVOURITEMODEL}.yml

EXERCISE:

Change precision of calculations and reuse factor in the keras configuration file and check effect on NN performance (AUC) and FPGA resource usage using the scripts above.

ReuseFactor: N  # N = number of times a multiplier is used to do a computation 
DefaultPrecision: ap_fixed<X,Y>  # X = total number of bits, Y = number of integer bits, X-Y = number of decimal bits

NB: suggest to change the project output directory in the keras configuration for each test to avoid overwriting of previous projects.