hls4ml

The goal of hls4ml is to provide an efficient and fast translation of machine learning models from open-source packages (like Keras and PyTorch) for training machine learning algorithms to high level synthesis (HLS) code that can then be transpiled to run on an FPGA. The resulting HLS project can be then used to produce an IP which can be plugged into more complex designs or be used to create a kernel for CPU co-processing. The user has freedom to define many of the parameters of their algorithm to best suit their needs.

The hls4ml package enables fast prototyping of a machine learning algorithm implementation in FPGAs, greatly reducing the time to results and giving the user intuition for how to best design a machine learning algorithm for their application while balancing performance, resource utilization and latency requirements.