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

TL;DR

CFU Playground is an open-source framework that streamlines the design, customization, and evaluation of FPGA-based ML accelerators, enabling rapid prototyping and significant performance improvements for embedded ML systems.

Contribution

It introduces a comprehensive open-source full-stack tool for hardware-software co-design and optimization of ML accelerators on FPGAs, facilitating rapid development and evaluation.

Findings

Achieved speedups of 55x to 75x in ML accelerator performance.

Enabled automated exploration of the design space using Vizier.

Provided a flexible, end-to-end open-source flow for embedded ML hardware development.

Abstract

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.