CFU Playground：用于FPGA上微型机器学习（tinyML）加速的全栈开源框架
CFU Playground: Full-Stack Open-Source Framework for Tiny Machine Learning (tinyML) Acceleration on FPGAs
AI总结：
本文提出全栈开源框架CFU Playground，支持FPGA上嵌入式机器学习加速器的快速软硬件协同设计与评估，结合自动化黑盒优化探索设计空间，实现了55至75倍的显著加速。
摘要：
对神经网络高效处理的需求推动了硬件加速器的发展。专用硬件的日益普及凸显了对更敏捷的设计流程的需求，以实现软硬件协同设计和领域特定优化。在本文中，我们提出了CFU Playground：一个全栈开源框架，支持对嵌入式ML系统的机器学习（ML）加速器进行快速迭代的设计与评估。我们的工具为FPGA上的软硬件协同设计及未来系统研究提供了完全开源的端到端流程。该全栈框架使用户能够探索针对嵌入式ML进行定制和协同优化的实验性与定制化架构。我们快速部署-分析-优化的反馈循环，使ML软硬件开发者能以较少的定制化投入获得显著回报。使用CFU Playground的设计与评估循环，我们展示了55倍至75倍的显著加速。与加速器耦合的软CPU开启了两个组件之间全新的丰富设计空间，我们利用开源黑盒优化服务Vizier以自动化方式对此进行了探索。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
