POSE: Phase II: Building an Open-Source Ecosystem for Deep-Learning Hardware-Software Co-Design

POSE：第二阶段：构建深度学习软硬件协同设计的开源生态系统

• 批准号: 2303735
• 负责人: Yakun Sophia Shao
• 金额: $ 149.97万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2303735&HistoricalAwards=false
• 关键词: POSE; Phase; II; Building; Open

Domain-specific acceleration is one of the most promising approaches to further improve performance and energy efficiency applications like deep learning. Despite the large number of startups and large companies developing specialized hardware and software for deep learning, all the existing implementations are proprietary, without a viable, freely open-source deep-learning hardware-software stack. The lack of an open and shared ecosystem not only makes it extremely hard to compare different implementations, it also significantly slows innovation and increases design costs as every organization needs to start their implementations from scratch.The overall objective of this proposal is to establish an open-source ecosystem that enables the development of full-stack deep-learning systems at scale to build next-generation deep-learning platforms. If successful, the outcomes of this project point toward a future in which developers with a great idea in either deep-learning hardware or software can quickly evaluate, design, and demonstrate their idea in an end-to-end fashion on real hardware and software, significantly lowering the design cost and accelerating the pace of innovation. Specifically, the open-source product consists of three key components based on our mature research projects: Exo for code generation (https://github.com/exo-lang/exo), Gemmini for deep-learning accelerator design (https://github.com/ucb-bar/gemmini), and Chipyard for system-on-chip integration (https://github.com/ucb-bar/chipyard). In particular, the proposed hardware-software ecosystem will fundamentally address challenges in 1) how to evaluate the end-to-end performance of deep-learning accelerators with the software stack; 2) how to generate efficient deep-learning hardware accelerators for specific scenarios; and 3) how to integrate accelerators with general-purpose cores and evaluate them in FPGA and fabrication. The hardware-software ecosystem established from this project will create a diverse and highly motivated open-source community for future developments on deep-learning acceleration through hardware-software co-design.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

特定于域的加速度是进一步提高性能和能源效率应用（例如深度学习）的最有希望的方法之一。尽管大量的初创公司和大型公司开发了专门的硬件和软件以进行深度学习，但所有现有的实施都是专有的，没有可行的，自由开放的深层学习硬件软件堆栈。缺乏开放和共享的生态系统不仅很难比较不同的实现，而且由于每个组织都需要从头开始实施其实施，因此也大大减慢了创新并增加了设计成本。该建议的总体目标是建立一个开放源代码生态系统，以建立一个能够在大规模建立下一代深层实现深层实现深层实现深度学习系统的开发。如果成功的话，这个项目的结果指向了未来的结果，在该未来中，具有深入学习硬件或软件的好主意的开发人员可以快速评估，设计和以端到端的方式来评估，设计和展示他们的想法，以实现真实硬件和软件，从而大大降低设计成本并加速创新的速度。具体而言，开源产品由基于我们成熟的研究项目的三个关键组件组成：代码生成的EXO（https://github.com/exo-lang/exo），gemmini，for deep-learning Accelerator设计（https：//github.com/github.com/ucb-bar/gemmini），以及for System-chipy-for System-for System-chip for System-Chipyard。 （https://github.com/ucb-bar/chipyard）。特别是，提出的硬件软件生态系统将从根本上解决1）如何通过软件堆栈评估深度学习加速器的端到端性能； 2）如何为特定方案生成有效的深度学习硬件加速器； 3）如何将加速器与通用核心集成在一起，并在FPGA和制造中对其进行评估。通过该项目建立的硬件软件生态系统将创建一个多样化且积极进取的开源社区，以通过硬件 - 软件软件共同设计进行深入学习加速的未来发展。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子和更广泛影响的评估来评估CRITERIA CRITERIA的评估。