CFU Playground: Full-Stack Open-Source Framework for Tiny Machine Learning (tinyML) Acceleration on FPGAs
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}
}
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.
January 9, 2022 by hgpu
