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CFU Playground: Full-Stack Open-Source Framework for Tiny Machine Learning (tinyML) Acceleration on FPGAs

Shvetank Prakash, Tim Callahan, Joseph Bushagour, Colby Banbury, Alan V. Green, Pete Warden, Tim Ansell, Vijay Janapa Reddi
Google
arXiv:2201.01863 [cs.LG], (5 Jan 2022)

@misc{prakash2022cfu,

   title={CFU Playground: Full-Stack Open-Source Framework for Tiny Machine Learning (tinyML) Acceleration on FPGAs},

   author={Shvetank Prakash and Tim Callahan and Joseph Bushagour and Colby Banbury and Alan V. Green and Pete Warden and Tim Ansell and Vijay Janapa Reddi},

   year={2022},

   eprint={2201.01863},

   archivePrefix={arXiv},

   primaryClass={cs.LG}

}

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We present CFU Playground, a full-stack open-source framework that enables rapid and iterative design of machine learning (ML) accelerators for embedded ML systems. Our toolchain tightly integrates open-source software, RTL generators, and FPGA tools for synthesis, place, and route. This full-stack development framework gives engineers access to explore bespoke architectures that are customized and co-optimized for embedded ML. The rapid, deploy-profile-optimization feedback loop lets ML hardware and software developers achieve significant returns out of a relatively small investment in customization. Using CFU Playground’s design loop, we show substantial speedups (55x-75x) and design space exploration between the CPU and accelerator.
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