Collaborative Research: CNS Core: Small: NV-RGRA: Non-Volatile Nano-Second Right-Grained Reconfigurable Architecture for Data-Intensive Machine Learning and Graph Computing

合作研究：CNS 核心：小型：NV-RGRA：用于数据密集型机器学习和图计算的非易失性纳秒右粒度可重构架构

• 批准号: 2228240
• 负责人: Houman Homayoun
• 金额: $ 29.07万
• 依托单位: University of California-Davis
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2228240&HistoricalAwards=false
• 关键词: Collaborative; Research; CNS; Core; Small

In the era of digital data, computing devices amass vast amounts of data continuously over time. This leads to a paradigm shift in the computing by adopting machine learning (ML) and graph analytic processing to analyze such massive amounts of data. Traditional computing paradigms are inefficient in terms of energy consumption, latency, and computational efficiency. Existing in-memory computing paradigms address this challenge to a certain extent, but are not adaptable for heterogeneous applications. The proposed project introduces a novel computing architecture, right-grained reconfigurable architecture (RGRA) that combines the flexibility of coarse-grained reconfigurable array (CGRA) and programmability of FPGAs, deploying a circuit-switched interconnects and router network with torus topology to address this challenge. At the top-level, RGRA is a many-core architecture with each core configurable at finer granularity. The proposed research is also expected to lead to the development of new branch of reconfigurable architectures that support efficient execution of data-intensive applications such as graph analytics and benefit from architectural aspects such as reconfigurability and heterogeneity. In terms of broader impact, design of hardware accelerators is one of the driving directions in the field of computer architecture. As such, the successful design of RGRA that is performance efficient irrespective of the application memory-traits can have a significant societal and economic impact. For instance, it can augment CPUs, FPGAs, and GPUs in the existing and emerging systems. The results of the project will include design of high-speed reconfigurable NVMs and interconnects, which can also be adopted in many-core systems towards developing high-throughput processors. With ML being taught in the higher-secondary schools, the project has a good scope for outreach to the community and attract students, especially in terms of (i) recruitment of underrepresented classes including minorities and women; (ii) outreach in the form of K-12 and undergraduate student involvement in research via summer internships and senior-design projects; and (iii) offering a graduate course on ML accelerator 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.

在数字数据时代，计算设备随着时间的推移不断积累大量数据。 通过采用机器学习（ML）和图形分析处理来分析如此大量的数据，这导致了计算的范式转变。传统的计算模式在能耗、延迟和计算效率方面是低效的。现有的内存计算范式在一定程度上解决了这一挑战，但不适用于异构应用程序。该项目提出了一种新的计算架构，右粒度可重构架构（RGRA），结合了粗粒度可重构阵列（CGRA）的灵活性和FPGA的可编程性，部署电路交换互连和环形拓扑路由器网络来解决这一挑战。在顶层，RGRA是一个众核架构，每个核心都可以更细的粒度进行配置。拟议的研究也有望导致新的可重构架构的分支的发展，支持数据密集型应用程序，如图形分析的有效执行，并受益于架构方面，如可重构性和异构性。 就更广泛的影响而言，硬件加速器的设计是计算机体系结构领域的驱动方向之一。因此，无论应用程序存储器特性如何，RGRA的性能高效的成功设计都可以具有显著的社会和经济影响。例如，它可以增强现有和新兴系统中的CPU、FPGA和GPU。该项目的成果将包括高速可重构NVM和互连的设计，这些设计也可以用于众核系统，以开发高吞吐量处理器。 由于在高中教授ML，该项目有很好的机会与社区联系并吸引学生，特别是在以下方面：㈠招聘代表性不足的班级，包括少数民族和妇女; ㈡以K-12和本科生通过暑期实习和高级设计项目参与研究的形式进行联系;以及（iii）提供ML加速器设计的研究生课程。该奖项反映了NSF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。