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Accessible machine learning algorithms, software, and diagnostic tools forenergy-efficient devices and systems are extremely valuable across a broadrange of application domains. In scientific domains, real-time near-sensorprocessing can drastically improve experimental design and acceleratescientific discoveries. To support domain scientists, we have developed hls4ml,an open-source software-hardware codesign workflow to interpret and translatemachine learning algorithms for implementation with both FPGA and ASICtechnologies. We expand on previous hls4ml work by extending capabilities andtechniques towards low-power implementations and increased usability: newPython APIs, quantization-aware pruning, end-to-end FPGA workflows, longpipeline kernels for low power, and new device backends include an ASICworkflow. Taken together, these and continued efforts in hls4ml will arm a newgeneration of domain scientists with accessible, efficient, and powerful toolsfor machine-learning-accelerated discovery.

hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices