SRAM存内计算技术综述

龚龙庆; 徐伟栋; 娄冕

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SRAM存内计算技术综述

专题与综述 | 更新时间：2026-09-04

- SRAM存内计算技术综述
- An overview of SRAM in-memory computing
- 微电子学与计算机 2021年38卷第9期页码：1-7
- 作者机构：
  
  西安微电子技术研究所,陕西,西安,710054
- 作者简介：
- 基金信息：
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- 纸质出版：2021
- 稿件说明：
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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]. 2021, 38(9): 1-7.
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. DOI：

GONG Longqin, XU Weidong, LOU Mian. An overview of SRAM in-memory computing[J]. 2021, 38(9): 1-7. DOI：

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.

Abstract

In the process of processing data-intensive applications such as deep neural networks

the frequent transfer of large amounts of data between the processor and the memory causes severe performance loss and energy consumption

which is the biggest bottleneck of the current von Neumann architecture. In view of the limitations of the traditional von Neumann architecture

the SRAM-based in-memory computing technology integrates the computing unit into the memory to support data storage and calculation

which completely breaks through the von Neumann bottleneck and is expected to become a new generation Intelligent computing architecture.This paper clarifies the problems of "power wall" and "storage wall" caused by the von Neumann architecture from the perspective of architecture

and gives the reasons for the rise of in-memory computing. The paper focuses on the research of SRAM-based in-memory computing architectures in recent years

and describes the working mechanism

advantages and disadvantages and significance of various SRAM-based in-memory computing architectures by taking several classical architectures as examples. And from the perspective of device level

circuit level and architecture level

the key factors of current SRAM-based in-memory computing technology are summarized respectively. The SRAM-based in-memory computing technology is a promising and versatile technology that will provide efficient and low-energy system architectures for machine learning applications

graph computing applications and genetic engineering. The paper looks forward to the development of SRAM-based in-memory computing technology in devices

circuits and architectures in the coming year

关键词

Keywords

references

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