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.

该项目研究了一种可扩展计算基础设施的设计，该基础设施使用纳米级非易失性存储器（NVM）设备进行存储和计算。该项目的新颖之处在于(i)在NVM横杆中通过自然发生的偷偷路径使用多个并行电流进行计算；（ii）用自动化合成技术取代缓慢的有机专家驱动的基于流的计算设计发现，以加速发现新的NVM横杆设计；（iii）在精确、近似和随机流计算设计中普遍关注容错。该项目的影响是：(i)设计了一个端到端框架，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交叉栏设计上；（ii）创建了一种新的可扩展功能，可以在易发生故障的纳米级NVM交叉栏上执行精确和近似的内存数字计算。计算机科学家和纳米科学研究人员组成的团队正在为四个基准问题创建基于流的计算设计：费曼大奖问题、计算机视觉、基本线性代数和动力系统模拟。自动合成的NVM横杆设计正在现代纳米技术实验室中使用高性能模拟和实验基准进行评估。通过存储在纳米级交叉棒中的数据，使用多个并行电流流进行计算通常更快，更节能，但这种交叉棒的设计对人类设计师来说非常不直观。该项目探索了检验布尔公式可满足性的形式化方法和人工智能技术（如最佳优先搜索）的结合，以自动从用高级编程语言编写的规范中合成NVM交叉栏设计。计算机科学家和纳米科学研究人员团队正在通过利用NVM横条中的存储设备作为结构约束易故障的分布式纳米数据存储，并利用通过NVM横条的自然并行电流对存储在分布式纳米存储中的数据进行计算，来追求极端规模计算的变革议程。由OpenCV、LAPACK和ODEINT程序生成的NVM交叉杆设计使用Xyce电路仿真软件进行评估，随后制作用于实验基准测试。通过在同一设备上结合存储和计算，该项目绕过了处理器和内存之间的冯·诺伊曼屏障，并在不引入对编程模型进行实质性更改的情况下，为易出错的NVM交叉条上的极端规模计算创建了可扩展的解决方案。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。