SPX: Scalable In-Memory Processing Using Spintronics

SPX：使用自旋电子学的可扩展内存处理

• 批准号: 1725420
• 负责人: Ulya Karpuzcu
• 金额: $ 80万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-15 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1725420&HistoricalAwards=false
• 关键词: SPX; Scalable; Memory; Processing; Using

The computational demands of modern workloads are influenced by a data-centric view of computing. The traditional model of computing, which brought the data into the compute engine for processing, is falling apart in the era of exploding data volumes as the overheads of data transportation become forbidding. Instead, it is more advantageous to take computing to the data. The objective of this project is to explore the alternative paradigm of bringing computation to the data by developing a novel scalable framework for processing-in-memory (PIM). While traditional CMOS structures are unsuited to this tight integration, emerging spintronic technologies show remarkable versatility in this regard. The proposed approach will develop the notion of the computational RAM (CRAM) to build PIM solutions to solve data-intensive computing problems using spintronics technologies. The project seeks to provide a complete solution across the system stack to the PIM problem under the CRAM platform. The project seeks to advance the state of the art in electronics technology, and potentially has a large impact in a pervasively-electronic society. Technically, its research results are projected to significantly advance the state of the art in large scale memory-centric computing using post-CMOS spintronic technologies, paving the way for new ways to build energy-efficient, scalable integrated systems. A multi-pronged outreach strategy will be pursued to take the results of this effort to a set of core constituencies. Human resource development will be achieved by training of undergraduate and graduate students in post-CMOS methods and novel computing paradigms.The notion of bringing computation nearer to memory has gained wide currency in the recent past. However, since the regularity of large memory arrays is considered sacrosanct, the most viable solutions proposed so far perform processing near-memory, performing computation at the edge of a large memory array. The proposed CRAM-based approach avoids the substantial overheads of such a method, in bringing data to and from the periphery, and proposes a method for reconfiguring the memory to write the output of a logic operation directly into a memory cell. This project realizes the potential of the CRAM across the system stack by exploring the optimum over a space of choices in technology, logic design, and memory architecture to implement a diverse set of basic computational building blocks; by quantitatively characterizing CRAM-specific multi-granular parallelism; by investigating implications for the eco-system integration; by devising effective methods for CRAM-specific spatio-temporal parallel task scheduling; and by demonstrating how bioinformatics applications and applications featuring irregular, i.e., amorphous parallelism can benefit from CRAM.

现代工作负载的计算需求受到以数据为中心的计算视图的影响。传统的计算模式将数据输入计算引擎进行处理，随着数据传输的开销变得令人生畏，这种模式在数据量爆炸式增长的时代正在瓦解。相反，对数据进行计算是更有利的。该项目的目标是通过开发一种新的可扩展内存处理（PIM）框架，探索将计算引入数据的替代范例。虽然传统的CMOS结构不适合这种紧密集成，但新兴的自旋电子技术在这方面显示出显着的多功能性。所提出的方法将发展计算RAM （CRAM）的概念，以构建PIM解决方案来解决使用自旋电子学技术的数据密集型计算问题。该项目旨在为CRAM平台下的PIM问题提供一个跨系统堆栈的完整解决方案。该项目旨在推动电子技术的发展，并可能在一个无处不在的电子社会中产生重大影响。从技术上说，其研究成果预计将显著推动采用后cmos自旋电子技术的大规模存储中心计算技术的发展，为构建节能、可扩展的集成系统铺平道路。将采取一项多管齐下的外联战略，将这一努力的成果带给一系列核心选民。人力资源开发将通过培养本科生和研究生学习后cmos方法和新的计算范式来实现。使计算更接近内存的概念在最近得到了广泛的传播。然而，由于大型内存阵列的规律性被认为是神圣不可侵犯的，迄今为止提出的最可行的解决方案是执行近内存处理，在大型内存阵列的边缘执行计算。所提出的基于ram的方法避免了这种方法在将数据从外围传输到外围时的大量开销，并提出了一种重新配置内存以将逻辑操作的输出直接写入内存单元的方法。该项目通过探索技术、逻辑设计和内存架构的最佳选择空间，实现了跨系统堆栈的CRAM潜力，以实现各种基本计算构建块；定量表征cram特有的多颗粒并行性；通过调查对生态系统整合的影响；通过设计有效的面向cram的时空并行任务调度方法；并通过演示生物信息学应用和具有不规则，即无定形并行性的应用如何从CRAM中受益。

项目成果

Spiking Neural Networks in Spintronic Computational RAM

• DOI: 10.1145/3475963
• 发表时间: 2020-06
• 期刊: ACM Transactions on Architecture and Code Optimization (TACO)
• 作者: Husrev Cilasun;Salonik Resch;Z. Chowdhury;Erin Olson;Masoud Zabihi;Zhengyang Zhao;Thomas J. Peterson;K. Parhi;Jianping Wang;S. Sapatnekar;Ulya R. Karpuzcu

A DNA Read Alignment Accelerator Based on Computational RAM

• DOI: 10.1109/jxcdc.2020.2987527
• 发表时间: 2020-04
• 期刊: IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
• 影响因子: 2.4
• 作者: Z. Chowdhury;Masoud Zabihi;S. K. Khatamifard;Zhengyang Zhao;Salonik Resch;Meisam Razaviyayn;Jianping Wang;S. Sapatnekar;Ulya R. Karpuzcu

CAMeleon: Reconfigurable B(T)CAM in Computational RAM

• DOI: 10.1145/3453688.3461507
• 发表时间: 2021-06
• 期刊: Proceedings of the 2021 Great Lakes Symposium on VLSI
• 作者: Z. Chowdhury;Salonik Resch;Husrev Cilasun;Zhengyang Zhao;Masoud Zabihi;Sachin S. Sapatnekar;Jianping Wang;Ulya R. Karpuzcu

MOUSE: Inference In Non-volatile Memory for Energy Harvesting Applications

• DOI: 10.1109/micro50266.2020.00042
• 发表时间: 2020-10
• 期刊: 2020 53rd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO)
• 作者: Salonik Resch;S. K. Khatamifard;Z. Chowdhury;Masoud Zabihi;Zhengyang Zhao;Hüsrev Cılasun;Jianping Wang;S. Sapatnekar;Ulya R. Karpuzcu

Analyzing the Effects of Interconnect Parasitics in the STT CRAM In-Memory Computational Platform

分析 STT CRAM 内存计算平台中互连寄生效应的影响

• DOI: 10.1109/jxcdc.2020.2985314
• 发表时间: 2020
• 期刊: IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
• 影响因子: 2.4
• 作者: Zabihi, Masoud;Sharma, Arvind K.;Mankalale, Meghna G.;Chowdhury, Zamshed Iqbal;Zhao, Zhengyang;Resch, Salonik;Karpuzcu, Ulya R.;Wang, Jian-Ping;Sapatnekar, Sachin S.
• 通讯作者: Sapatnekar, Sachin S.