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 §Purdue University ∗Harvard University

Abstract: 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 (55×-75×) and design space exploration between the CPU and accelerator.

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