CFU Playground: Full-Stack Open-Source Framework for Tiny Machine Learning (tinyML) Acceleration on FPGAs
This provides a tool for embedded ML developers to rapidly design and optimize custom accelerators, though it is incremental as it builds on existing co-design concepts.
The authors tackled the need for agile hardware-software co-design in tinyML by presenting CFU Playground, an open-source framework for accelerating ML on FPGAs, achieving speedups of 55× to 75×.
Need for the efficient processing of neural networks has given rise to the development of hardware accelerators. The increased adoption of specialized hardware has highlighted the need for more agile design flows for hardware-software co-design and domain-specific optimizations. In this paper, we present CFU Playground: a full-stack open-source framework that enables rapid and iterative design and evaluation of machine learning (ML) accelerators for embedded ML systems. Our tool provides a completely open-source end-to-end flow for hardware-software co-design on FPGAs and future systems research. This full-stack framework gives the users access to explore experimental and bespoke architectures that are customized and co-optimized for embedded ML. Our 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 and evaluation loop, we show substantial speedups between 55$\times$ and 75$\times$. The soft CPU coupled with the accelerator opens up a new, rich design space between the two components that we explore in an automated fashion using Vizier, an open-source black-box optimization service.