hls4ml: An open-source codesign workflow to empower scientific low-power machine learning devices

Abstract

Accessible machine learning algorithms, software, and diagnostic tools for energy-efficient devices and systems are extremely valuable across a broad range of application domains. In scientific domains, real-time near-sensor processing can drastically improve experimental design and accelerate scientific discoveries. To support domain scientists, we have developed hls4ml, an open-source software-hardware codesign workflow to interpret and translate machine learning algorithms for implementation with both FPGA and ASIC technologies. We expand on previous hls4ml work by extending capabilities and techniques towards low-power implementations and increased usability: new Python APIs, quantization-aware pruning, end-to-end FPGA workflows, long pipeline kernels for low power, and new device backends include an ASIC workflow. Taken together, these and continued efforts in hls4ml will arm a new generation of domain scientists with accessible, efficient, and powerful tools for machine-learning-accelerated discovery.

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BibTeX

@article{fahim2021hls4ml,
    author = "Fahim, Farah and Hawks, Benjamin and Herwig, Christian and Hirschauer, James and Jindariani, Sergo and Tran, Nhan and Carloni, Luca P and Di Guglielmo, Giuseppe and Harris, Philip and Krupa, Jeffrey and others",
    title = "hls4ml: An open-source codesign workflow to empower scientific low-power machine learning devices",
    journal = "arXiv preprint arXiv:2103.05579",
    year = "2021"
}

Recommended citation: Farah Fahim, Benjamin Hawks, Christian Herwig, James Hirschauer, Sergo Jindariani, Nhan Tran, Luca P Carloni, Giuseppe Di Guglielmo, Philip Harris, Jeffrey Krupa, others (2021). "hls4ml: An open-source codesign workflow to empower scientific low-power machine learning devices." arXiv preprint arXiv:2103.05579.
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