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hls4ml - Open Source Machine Learning Accelerators on FPGAs - Hawks, Meza

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  • hls4ml - Open Source Machine Learning Accelerators on FPGAs - Hawks, Meza

    hls4ml - Open Source Machine Learning Accelerators on FPGAs - Hawks, Meza

    An open-source Python package.

    hls4ml - Open Source Machine Learning Accelerators on FPGAs

    Ben Hawks, Andres Meza

    Born from the high energy physics community at the Large Hadron Collider, hls4ml is an open-source Python package for machine learning inference in FPGAs (Field Programmable Gate Arrays). It creates firmware implementations of machine learning algorithms by translating traditional, open-source machine learning package models into optimized high level synthesis C++ that can then be customized for your use case and implemented on devices such as FPGAs and Application Specific Integrated Circuits (ASICs). Hls4ml can easily scale the implementation of a model to take advantage of the parallel processing capabilities that FPGAs offer, not only allowing for low latency, high throughput designs, but also designs sized to fit on lower cost, resource constrained hardware. Hls4ml also supports generating accelerators with different drivers that build minimal, self-contained implementations which enable control via Python or C/C++ with little extra development or hardware expertise.

    Ben Hawks is an AI Researcher at Fermi National Accelerator Laboratory, focusing on optimizing and compressing neural networks to be tiny, fast, and accurate for use on FPGAs and other specialized hardware. Since he was young, he’s had a personal interest in computer security, programming, and electronics, and is interested in learning how to make machine learning fair, efficient, and fast. Outside of work, he spends his time messing with electronics, tabletop RPGs, and catering to the whims of a small feline overlord.

    Andres Meza is a research and development engineer in the Department of Computer Science and Engineering at the University of California, San Diego. He received a B.S. Computer Science and a B.S. Cognitive Science with a Machine Learning and Neural Computation Specialization from UCSD in 2020. His current research focuses on hardware security, optimization of ML models for hardware deployment, and computer vision.