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: 1823015
• 负责人: Nathaniel Cady
• 金额: $ 50万
• 依托单位: SUNY Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1823015&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）使用通过 NVM 交叉开关中自然存在的潜行路径的多个并行电流进行计算； (ii) 用自动合成技术取代缓慢的专家驱动的基于流程的计算设计的发现，以加速发现新颖的 NVM 交叉设计； (iii) 在精确、近似和随机基于流的计算设计的整个设计过程中普遍关注容错性。该项目的影响是 (i) 设计一个端到端框架，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 交叉开关设计上，以及 (ii) 创建新的可扩展功能来执行精确的操作并对容易出现故障的纳米级 NVM 交叉开关进行近似内存数字计算。由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 交叉开关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。使用通过纳米级交叉开关中存储的数据的多个并行电流进行计算通常速度快且更节能，但这种交叉开关的设计对于人类设计者来说非常不直观。该项目探索了检查布尔公式可满足性的形式化方法与最佳优先搜索等人工智能技术的结合，以根据用高级编程语言编写的规范自动合成 NVM 交叉开关设计。由计算机科学家和纳米科学研究人员组成的团队正在追求超大规模计算的变革议程，利用 NVM 交叉开关中的存储设备作为结构受限、易于故障的分布式纳米数据存储，并利用流经 NVM 交叉开关的自然并行电流用于对存储在分布式纳米存储中的数据进行计算。使用 Xyce 电路仿真软件对 OpenCV、LAPACK 和 ODEINT 程序生成的 NVM 交叉开关设计进行评估，然后进行制作以进行实验基准测试。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·诺依曼屏障，并为容易出错的 NVM 交叉开关上的超大规模计算创建了可扩展的解决方案，而无需对编程模型进行实质性更改。该奖项反映了通过使用基金会的智力价值和更广泛的影响审查标准进行评估，NSF 的法定使命被认为值得支持。

项目成果

Detecting Temporal Correlation on HfO 2 Based RRAM on 65nm CMOS Technology

采用 65nm CMOS 技术检测基于 HfO 2 的 RRAM 的时间相关性

• DOI: 10.1109/mdts54894.2022.9826965
• 发表时间: 2022-08
• 期刊: 2022 IEEE 31st Microelectronics Design & Test Symposium (MDTS
• 作者: Rafiq, Sarah;Abedin, Minhaz;Beckmann, Karsten;Cady, Nathaniel C.
• 通讯作者: Cady, Nathaniel C.

Investigation of ReRAM Variability on Flow-Based Edge Detection Computing Using HfO 2 -Based ReRAM Arrays

使用基于 HfO 2 的 ReRAM 阵列研究基于流的边缘检测计算的 ReRAM 可变性

• DOI: 10.1109/tcsi.2021.3072210
• 发表时间: 2021-07
• 期刊: IEEE Transactions on Circuits and Systems I: Regular Papers
• 作者: Rafiq, Sarah;Hazra, Jubin;Liehr, Maximilian;Beckmann, Karsten;Abedin, Minhaz;Pannu, Jodh S.;Jha, Sumit K.;Cady, Nathaniel C.
• 通讯作者: Cady, Nathaniel C.

In-memory Computation of Error-Correcting Codes Using a Reconfigurable HfOx ReRAM 1T1R Array

使用可重新配置的 HfOx ReRAM 1T1R 阵列进行纠错码的内存计算

• DOI: 10.1109/mwscas47672.2021.9531717
• 发表时间: 2021-08
• 期刊: 2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS
• 作者: Abedin, Minhaz;Liehr, Maximilian;Beckmann, Karsten;Hazra, Jubin;Rafiq, Sarah;Cady, Nathaniel C.
• 通讯作者: Cady, Nathaniel C.