A tutorial and reference design for machine learning inference on FPGA-based frame grabber devices in high-throughput imaging applications. This tutorial leverages the hls4ml package and the CustomLogic toolkit to deploy neural networks to Euresys frame grabber devices. Refer to hls4ml-frame-grabber-tutorial.ipynb to get started. See part9_FOLO_frame_grabbers_advanced_features.ipynb for a more advanced guide on implementing a YOLO-style model and taking advantage of the suite of optimizations hls4ml provides.
To install hls4ml_frame_grabber conda environment
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conda env create -f environment.yml -
conda activate hls4ml_frame_grabber -
ipython kernel install --user --name=hls4ml_frame_grabber
We have also released hls4ml-frame-grabber-tutorial.ipynb in a medium article format which can be found here.
Primary development was completed by Fermi National Accelerator Laboratory, Northwestern University, and Drexel University. This work resulted from the implementation described in this paper: https://arxiv.org/abs/2312.00128. Deepest thanks to Euresys and the Columbia University HBT-EP group for their assistance and contribution to this development.