CAREER: Generating Domain-Specific Systems at Scale

职业：大规模生成特定领域的系统

• 批准号: 2238346
• 负责人: Yakun Sophia Shao
• 金额: $ 60万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2028-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2238346&HistoricalAwards=false
• 关键词: CAREER; Generating; Domain; Specific; Systems

In the era of diminishing returns from technology scaling, hardware acceleration is one of the most promising approaches to deliver improved performance and energy efficiency for future computing platforms. However, state-of-the-art accelerator research has largely focused on individual accelerator development for a handful of algorithms, without a systematic understanding of how to holistically generate many accelerators at scale while improving the overall execution quality. To address the limitation, the goal of this project is to develop a quality-driven accelerator design flow that enables the holistic simulation, design, and integration of domain-specific accelerators at scale for end-to-end applications. It will especially benefit emerging real-time applications, such as robotics and self-driving vehicles, which feature a range of computationally demanding kernels. In addition, the integrated education activities will broaden participation in computing at all levels with outreach activities to engage K-12, undergraduate, and graduate students in exciting opportunities in the hardware industry. This project will advance the state of knowledge in computer architecture on how to effectively generate domain-specific systems at scale to achieve end-to-end acceleration. Toward this goal, the project investigates the following three synergistic research thrusts: 1) design a hardware-software co-simulation infrastructure to quantitatively evaluate the end-to-end performance of closed-loop autonomous systems; 2) develop a holistic design-space exploration framework to efficiently construct, represent, and navigate the complex design space of entire systems-on-chip; and 3) build an adaptive contention management system through hardware-software co-design to dynamically partition shared resources between accelerators. The methodology and framework the project develops will be open-sourced and enable accelerator integration at a greater scale than what can be done today, fundamentally advancing the field of heterogeneous system 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.

在技术扩展带来的收益递减的时代，硬件加速是最有希望为未来的计算平台提供改进性能和能源效率的方法之一。然而，最先进的加速器研究主要集中在少数算法的单个加速器开发上，而没有系统地了解如何大规模地整体生成许多加速器，同时提高整体执行质量。为了解决这一限制，这个项目的目标是开发一个质量驱动的加速器设计流，使特定领域的加速器能够在端到端应用程序的规模上进行整体模拟、设计和集成。它将特别有利于新兴的实时应用，如机器人和自动驾驶汽车，这些应用具有一系列计算要求高的内核。此外，综合教育活动将通过拓展活动，使K-12、本科生和研究生在硬件行业获得令人兴奋的机会，从而扩大各个层次对计算机的参与。该项目将推进计算机体系结构中如何有效地大规模生成特定领域系统以实现端到端加速的知识状态。为了实现这一目标，该项目研究了以下三个协同研究重点：1)设计一个软硬件联合仿真基础设施，以定量评估闭环自主系统的端到端性能；2)开发一个整体的设计空间探索框架，以有效地构建、表示和导航整个片上系统的复杂设计空间；3)通过软硬件协同设计，构建自适应争用管理系统，实现加速器间共享资源的动态分区。该项目开发的方法和框架将是开源的，能够实现比现在更大规模的加速器集成，从根本上推动异构系统设计领域的发展。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。