SHF:SMALL:Collaborative Research: Exploring Nonvolatility of Emerging Memory Technologies for Architecture Design

SHF:SMALL:合作研究：探索新兴内存技术的非易失性以用于架构设计

• 批准号: 1816833
• 负责人: Yuan Xie
• 金额: $ 24.9万
• 依托单位: University of California-Santa Barbara
• 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: 2018-08-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1816833&HistoricalAwards=false
• 关键词: SHF; SMALL; Collaborative; Research; Exploring

In modern computers, by combining the speed of traditional cache technology, the density of traditional main memory technology, and the non-volatility of flash memory, a new class of emerging byte-addressable nonvolatile memories (NVMs) have great potential to be used as the universal memories of the future. Such memory types include technologies such as phase-change memory, spin-transfer-torque magnetoresistive memory, and resistive memory. As these emerging memory technologies mature, it is important for computer architects to understand their pros and cons in a comprehensive manner in order to improve the performance, power, and reliability of future computer systems incorporating these systems which will be used in various application domains. Yet, most of previous research on NVM architecture is focused only on the performance, power, and density benefits and how to overcome challenges, such as write overhead and wearout issues. The non-volatility characteristic of NVM technologies is not fully explored. Therefore, this project examines how to exploit the non-volatility characteristic that distinguishes the emerging NVM technologies from traditional memory technologies, and investigate new memory architecture design with novel applications.The goal of this project is to advance the memory architecture design of various types of computer systems with a full exploration of the non-volatility characteristic of NVM technologies across architecture, system, and application levels. To this end, the project explores the design space of various types of computer systems, ranging from severs to embedded systems. In particular, the project identifies and addresses design issues in nonvolatile cache architecture, re-architects main memory structure to leverage the non-volatility characteristic to improve system performance and energy consumption, supports persistent memory systems in various use cases with emerging NVM technologies, and studies near-data-computing techniques applied for these NVM technologies. The successful outcome of this research is expected to provide the design guidelines for enabling both large capacity and fast-bandwidth nonvolatile memory/storage, which are beyond the present state-of-the-art. Consequently, the research will spawn new applications involving the computation on the exascale of data, e.g., data mining, machine learning, visual or auditory sensory data recognition, bio-informatics, etc.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.

在现代计算机中，通过结合传统缓存技术的速度、传统主存储器技术的密度以及闪存的非易失性，一类新兴的字节可寻址非易失性存储器（NVM）具有作为未来通用存储器的巨大潜力。此类存储器类型包括诸如相变存储器、自旋转移矩磁阻存储器和电阻存储器之类的技术。随着这些新兴存储器技术的成熟，计算机架构师必须全面了解它们的优缺点，以便提高未来包含这些系统的计算机系统的性能、功耗和可靠性，并将其用于各种应用领域。然而，之前对 NVM 架构的大多数研究仅关注性能、功耗和密度优势以及如何克服写入开销和磨损问题等挑战。 NVM技术的非易失性特征尚未得到充分探索。因此，本项目研究如何利用新兴NVM技术区别于传统存储器技术的非易失性特征，并研究具有新颖应用的新型存储器架构设计。本项目的目标是通过跨架构、系统和应用层面全面探索NVM技术的非易失性特征，推进各类计算机系统的存储器架构设计。为此，该项目探索了从服务器到嵌入式系统的各种类型计算机系统的设计空间。 特别是，该项目识别并解决非易失性缓存架构中的设计问题，重新架构主存储器结构以利用非易失性特性来提高系统性能和能耗，利用新兴NVM技术支持各种用例中的持久性内存系统，并研究应用于这些NVM技术的近数据计算技术。这项研究的成功成果预计将为实现大容量和快速带宽非易失性存储器/存储提供设计指南，这超出了目前的最先进水平。因此，该研究将催生涉及百亿亿级数据计算的新应用，例如数据挖掘、机器学习、视觉或听觉感官数据识别、生物信息学等。该奖项反映了 NSF 的法定使命，并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，被认为值得支持。

项目成果

MEDAL: Scalable DIMM based Near Data Processing Accelerator for DNA Seeding Algorithm

• DOI: 10.1145/3352460.3358329
• 发表时间: 2019-10
• 期刊: Proceedings of the 52nd Annual IEEE/ACM International Symposium on Microarchitecture
• 作者: Wenqin Huangfu;Xueqi Li;Shuangchen Li;Xing Hu;P. Gu;Yuan Xie

Rescuing RRAM-Based Computing From Static and Dynamic Faults

• DOI: 10.1109/tcad.2020.3037316
• 发表时间: 2021-10-01
• 期刊: IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS
• 影响因子: 2.9
• 作者: Lin, Jilan;Wen, Cheng-Da;Xie, Yuan
• 通讯作者: Xie, Yuan

Improving Streaming Graph Processing Performance using Input Knowledge

• DOI: 10.1145/3466752.3480096
• 发表时间: 2021-10
• 期刊: MICRO-54: 54th Annual IEEE/ACM International Symposium on Microarchitecture
• 作者: Abanti Basak;Zheng Qu;Jilan Lin;Alaa R. Alameldeen;Zeshan A. Chishti;Yufei Ding;Yuan Xie

Overcoming the Memory Hierarchy Inefficiencies in Graph Processing Applications

克服图形处理应用程序中内存层次结构的低效率

• DOI: 10.1109/iccad51958.2021.9643434
• 发表时间: 2021
• 期刊: 2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD
• 作者: Lin, Jilan;Li, Shuangchen;Ding, Yufei;Xie, Yuan
• 通讯作者: Xie, Yuan

Brain-Inspired Computing: Adventure from Beyond CMOS Technologies to Beyond von Neumann Architectures

类脑计算：从超越 CMOS 技术到超越冯·诺依曼架构的冒险

• 发表时间: 2021
• 期刊: 2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD
• 作者: Amrouch, Hussam;Chen, Jian-Jia;Roy, Kaushik;Xie, Yuan;Chakraborty, Indranil;Huangfu, Wenqin Huangfu;Liang, Ling;Tu, Fengbin;Wang, Cheng;Yayla, Mikail
• 通讯作者: Yayla, Mikail