SPX: Collaborative Research: Automated Synthesis of Extreme-Scale Computing Systems Using Non-Volatile Memory

SPX：协作研究：使用非易失性存储器自动合成超大规模计算系统

• 批准号: 2408925
• 负责人: Sumit Jha
• 金额: $ 50万
• 依托单位: Florida International University
• 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: 2023-12-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2408925&HistoricalAwards=false
• 关键词: SPX; Collaborative; Research; Automated; Synthesis

The project investigates the design of a scalable computing infrastructure that uses nanoscale non-volatile memory (NVM) devices for both storage and computation. The project's novelties are (i) the use of multiple parallel flows of current through naturally occurring sneak paths in NVM crossbars for computation; (ii) the replacement of slow organic expert-driven discovery of flow-based computing designs by automated synthesis techniques for accelerated discovery of novel NVM crossbar designs; and (iii) a pervasive focus on fault-tolerance throughout the design of exact, approximate and stochastic flow-based computing designs. The project's impacts are (i) the design of an end-to-end framework that maps compute-intensive kernels written in a high-level programming language onto nanoscale NVM crossbar designs and (ii) the creation of a new scalable capability to perform exact and approximate in-memory digital computations on fault-prone nanoscale NVM crossbars. The team of computer scientists and nanoscience researchers is creating flow-based computing designs for four benchmark problems: the Feynman grand prize problem, computer vision, basic linear algebra, and simulation of dynamical systems. The automatically synthesized NVM crossbar designs are being evaluated using high-performance simulations and experimental benchmarking in a modern nanotechnology laboratory. Computing using multiple parallel flows of current through data stored in nanoscale crossbars is often fast and more energy-efficient, but the design of such crossbars is highly unintuitive for human designers. The project explores a combination of formal methods for checking satisfiability of Boolean formulae, and artificial intelligence techniques such as best-first search, to automatically synthesize NVM crossbar designs from specifications written in a high-level programming language. The team of computer scientists and nanoscience researchers is pursuing a transformative agenda for extreme-scale computing by leveraging memory devices in NVM crossbars as structurally-constrained fault-prone distributed nano-stores of data, and exploiting the natural parallel flow of current through NVM crossbars for computing over data stored in the distributed nano-stores. The NVM crossbar designs generated from OpenCV, LAPACK, and ODEINT programs are evaluated using the Xyce circuit simulation software and subsequently fabricated for experimental benchmarking. By combining storage and computation on the same device, the project circumvents the von Neumann barrier between the processor and the memory and creates scalable solutions for extreme-scale computing on fault-prone NVM crossbars without introducing substantial changes to the programming model.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.

该项目研究了使用纳米级非易失性内存（NVM）设备进行存储和计算的可扩展计算基础架构的设计。该项目的新颖性是（i）通过在NVM横杆中自然发生的偷偷摸摸的路径来使用多个平行流以进行计算； （ii）用自动合成技术替换了缓慢的有机专家驱动的基于流量的计算设计，以加速新型NVM横梁设计； （iii）在整个精确，近似和随机流动的计算设计的设计中，普遍地关注容错。该项目的影响是（i）端到端框架的设计，该框架将用高级编程语言编写的计算密集型内核映射到纳米级NVM NVM NVM跨键设计上，以及（ii）创建新的可扩展能力以执行精确的nansoscale nansoscale nansoscale nansoscale nvm nvm Crossbars的精确数字计算。计算机科学家和纳米科学研究人员的团队正在为四个基准问题创建基于流动的计算设计：Feynman大奖问题，计算机视觉，基本线性代数以及动态系统的模拟。正在使用现代纳米技术实验室中的高性能模拟和实验基准测试来评估自动合成的NVM横梁设计。通过在纳米级横杆中存储的数据进行多个电流流量的计算通常是快速且节能的，但是这种横梁的设计对人类设计师来说是高度不直觉的。该项目探讨了用于检查布尔公式满意度的形式方法的组合，以及人工智能技术（例如最佳优先搜索），以自动从用高级编程语言编写的规格中综合NVM横杆设计。计算机科学家和纳米科学研究人员的团队正在通过利用NVM横梁中的内存设备作为结构约束的数据的纳米存储，并利用NVM通过NVM Cross Bars的自然平行流动来计算在分布式的Nanano Stores中计算的数据，以利用NVM横梁中的内存设备来追求对极端计算的变革议程。使用XYCE电路仿真软件评估了从OpenCV，Lapack和Odeint程序生成的NVM横梁设计，然后用于实验基准测试。通过将存储和计算结合在同一设备上，该项目规避了处理器和内存之间的von Neumann屏障，并为在不实现故障的NVM横杆上进行极端规模的计算而创建了可扩展的解决方案，而无需对编程模型引入实质性更改。该奖项颁发了NSF的法定任务，并反映了通过评估概念的支持者的支持者，并反映了该奖励的范围，并已被评估范围。