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CFU Playground: Significant Speedups & Design Space Exploration Between CPU & Accelerator

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Technical paper titled “CFU Playground: Full-Stack Open-Source Framework for Tiny Machine Learning (tinyML) Acceleration on FPGAs,” from Google, Purdue University and 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 (55x-75x) and design space exploration between the CPU and accelerator.”

Find the technical paper here. Published Jan. 2022.

arXiv:2201.01863v1 Shvetank Prakash, Tim Callahan, Joseph Bushagour, Colby Banbury, Alan V. Green, Pete Warden, Tim Ansell, Vijay Janapa Reddi.

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