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SRAM存内计算技术综述

龚龙庆 徐伟栋 娄冕

龚龙庆, 徐伟栋, 娄冕. SRAM存内计算技术综述[J]. 微电子学与计算机, 2021, 38(9): 1-7.
引用本文: 龚龙庆, 徐伟栋, 娄冕. SRAM存内计算技术综述[J]. 微电子学与计算机, 2021, 38(9): 1-7.
GONG Longqin, XU Weidong, LOU Mian. An overview of SRAM in-memory computing[J]. Microelectronics & Computer, 2021, 38(9): 1-7.
Citation: GONG Longqin, XU Weidong, LOU Mian. An overview of SRAM in-memory computing[J]. Microelectronics & Computer, 2021, 38(9): 1-7.

SRAM存内计算技术综述

详细信息
    作者简介:

    龚龙庆  男,(1962-),研究员,博士生导师. 研究方向为空间嵌入式计算机设计与应用技术. E-mail: glq771@sina.com

    徐伟栋  男,(1993-),博士研究生. 研究方向为存内计算技术

    娄冕  男,(1987-),博士,高级工程师. 研究方向为IC设计技术

  • 中图分类号: TP3

An overview of SRAM in-memory computing

  • 摘要: 在处理深度神经网络这类数据密集型应用的过程中,处理器和存储器间大量数据的频繁传输会造成严重的性能损耗和能量消耗,也是当前冯·诺伊曼架构最大的瓶颈.针对传统冯·诺伊曼体系架构的局限性,基于SRAM的存内计算技术将运算单元集成到内存中,支持数据的即存即算,彻底突破了冯·诺伊曼瓶颈,有望成为新一代智能计算架构.本文从体系结构的角度阐明了冯·诺伊曼架构所引起的“功耗墙”和“存储墙”问题,并给出了存内计算技术的兴起原因.文章围绕近几年国内外关于SRAM存内计算架构的研究,以其中几种经典架构为例描述了各类SRAM存内计算的工作机理、优缺点及意义,并从器件级、电路级和架构级的角度分别概述了目前关于SRAM存内计算技术的关键影响因素.SRAM存内计算技术潜力巨大,用途广泛,将会给机器学习应用,图计算应用和基因工程提供高效低能耗的系统结构支持,最后展望了未来几年内SRAM存内计算技术在器件、电路和架构方面的发展情况.
  • 图  1  冯·诺伊曼架构和冯·诺伊曼瓶颈

    图  2  8T转置单元

    图  3  分裂式字线6T单元

    图  4  分比特乘法计算单元

    图  5  双生8T SRAM单元

    图  6  时域神经网络协处理器

    图  7  三明治式SRAM架构

    图  8  有限的信号容限

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出版历程
  • 收稿日期:  2021-01-20
  • 修回日期:  2021-03-10
  • 刊出日期:  2021-09-05

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