Some infrastructure for generating Vivado IP based on hls4ml & performing design-space exploration to find ideal hyperparameters

The most important components are generate_hls4ml_proj.sh and ip_sources/python/generate_hls4ml_proj.py. The former sets environment variables like the name of the project, the weights path, and some parameters used during hls4ml project creation. The latter is used to actually then generate the Vivado HLS project files for a particular neural network. Finally, manage_hls_projects.sh can be used to then build it and package it as an IP for use in Vivado.

Basic usage might look something like:

• Train a Keras-based neural network somehow and get some weights
• (Optionally) put the weights in ip_sources/python/weights and some test data in ip_sources/python/test_data
• Override load_model to load your model based on HLS4ML_WEIGHTS_PATH
• Override load_data to load testing data for your model based on HLS4ML_TEST_DATA_PATH and compare_predictions to calculate an accuracy/loss/etc that you would like to use to evaluate models
• Pick how you'd like your model to be quantized by setting the WORD_WIDTH and INT_WIDTH in setup_env.sh (which controls the "interface" precision or the word/int width used for I/O TDATA from your network) and default_precision (which specifies the word/int width used for other unspecified layers)
• Change the fpga_part
• Run generate_hls4ml_proj.sh to generate the Vivado HLS project
• Run manage_hls_projects.sh --build ${HLS4ML_PROJECT_NAME} to execute Vivado HLS
• Run manage_hls_projects.sh --export ${HLS4ML_PROJECT_NAME} to package and place your Vivado IP in the ip_artifacts folder, ready for use as a Vivado IP repository