存算一体技术研究现状

李嘉宁; 姚鹏; 揭路; 唐建石; 伍冬; 高滨; 钱鹤; 吴华强

doi:10.12263/DZXB.20230967

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存算一体技术研究现状

中国电子学会科学技术奖特约专栏 | 更新时间：2025-12-08

- 存算一体技术研究现状
- Research Status of Computing-in-Memory Technology
- 电子学报 2024年52卷第4期页码：1103-1117
- 作者机构：
  
  清华大学集成电路学院，北京 100084
- 作者简介：
  
  [ "李嘉宁男，2021年于吉林大学电子科学与工程学院获学士学位，2021年至今于清华大学集成电路学院攻读博士学位.目前的主要研究方向为存算一体电路与架构." ]
  [ "姚鹏男，2014年于西安交通大学获微电子学学士学位，2020年于清华大学获博士学位.已在《自然》、《科学》、《自然通讯》、ISSCC、IEDM和VLSI等期刊和会议上发表或合作发表了多篇论文.主要研究方向：存算一体技术、神经形态计算等." ]
  [ "揭路男，2013-2017年于浙江大学信息与电子工程学院获学士学位， 2021年于美国密歇根大学电气与计算机工程系获博士学位.现为集成电路学院助理教授.研究方向：模数/数模转换器、可重构数模混合电路、数模混合计算等." ]
  [ "唐建石男，2008年本科毕业于清华大学微纳电子系，2014年博士毕业于美国UCLA电子工程系，2015-2019年在美国IBM T. J. Watson Research Center工作，2019年回清华大学工作，现任清华大学集成电路学院副教授.主要研究方向包括新型存储器与类脑计算、单片三维异质集成等.中国电子学会会员编号：E190034508M." ]
  [ "伍冬男，2001年7月毕业于西安交通大学电子工程系，获学士学位，2006年7月毕业于清华大学微电子学研究所，获博士学位，现为集成电路学院副研究员.研究方向：主要从事图像传感器和非挥发性存储器等阵列式电路系统设计技术研究." ]
  [ "高滨男，2008年和2013年分别在北京大学获得物理学学士学位和微电子学博士学位.目前，目前在清华大学集成电路学院担任副教授.主要研究方向为新型半导体器件的制造、表征和理论建模，特别强调阻变随机存取存储器（RRAM）." ]
  [ "钱鹤男，1990年毕业于西安交通大学微电子专业并获博士学位；1990年12月～2006年5月在中科院微电子所工作，并于2001年9月～2006年5月任该所所长；2006年6月～2008年12月在三星半导体（中国）研究所工作，任所长；2009年1月起入职清华大学.科研工作主要集中在新型半导体存储器方面，包括面向嵌入式存储和安全认证应用，以及基于忆阻器的存算一体（CIM）芯片研发等." ]
  [ "吴华强男，2000年毕业于清华大学材料科学与工程系，获得工学学士学位；同年获清华大学经济管理学院管理学士学位（双学位）.2005年在美国康奈尔大学（Cornell University）电子与计算机工程学院获工学博士学位.随后先后在美国Spansion公司和美国Primet Precision Materials公司分别担任高级工程师和技术主管.2009年，加入清华大学微电子学研究所，现任清华大学集成电路学院院长.长期从事新型存储器及基于忆阻器的存算一体研究，涵盖了从器件、工艺集成、架构、算法、芯片以及系统等多个层次.中国电子学会会员编号：E190085238M.E-mail： wuhq@tsinghua.edu.cn" ]
- 基金信息：
  
  国家自然科学基金(92164302;62025111)
- DOI：10.12263/DZXB.20230967
  中图分类号： TP389.1
- 收稿：2023-10-19，
  
  修回：2024-04-03，
  
  纸质出版：2024-04-25
- 稿件说明：
移动端阅览
李嘉宁, 姚鹏, 揭路, 等. 存算一体技术研究现状[J]. 电子学报, 2024, 52(04): 1103-1117.

LI Jia-ning, YAO Peng, JIE Lu, et al. Research Status of Computing-in-Memory Technology[J]. Acta Electronica Sinica, 2024, 52(04): 1103-1117.
李嘉宁, 姚鹏, 揭路, 等. 存算一体技术研究现状[J]. 电子学报, 2024, 52(04): 1103-1117. DOI：10.12263/DZXB.20230967

LI Jia-ning, YAO Peng, JIE Lu, et al. Research Status of Computing-in-Memory Technology[J]. Acta Electronica Sinica, 2024, 52(04): 1103-1117. DOI：10.12263/DZXB.20230967

摘要

冯诺依曼计算机体系结构面临着“存储墙”的瓶颈，阻碍AI（Artificial Intelligence）计算性能提升.存算一体硬件结构打破了“存储墙”的限制，大大提升了AI计算的性能.目前存算一体计算方案已在多种存储介质上得到实现，根据计算信号类型，可以将存算一体计算方案分成数字存算一体方案和模拟存算一体方案.存算一体硬件结构使得AI计算的性能取得巨大提升，然而进一步发展仍面临重大挑战.本文对不同信号域的存算一体方案的进行了对比分析，指出了每一种方案的主要优缺点，也指明了存算一体技术面临的挑战.我们认为，随着工艺集成、器件、电路、架构，软件工具链的跨层次协同研究发展，存算一体技术将在边缘端和云端，为AI计算提供更加强大和高效的算力.

Abstract

Von Neumann computer architecture faces the bottleneck of “storage wall”

which hindering the performance improvement of AI (Artificial Intelligence) computing. Computing-In-Memory (CIM) breaks the limitation of “storage wall” and greatly improves the performance of AI computing. At present

CIM schemes have been implemented in a variety of storage media. According to the type of calculation signal

CIM scheme can be divided into digital CIM and analog CIM scheme. CIM has greatly improved the performance of AI computing

but the further development still faces major challenges. This article provides a detailed comparative analysis of CIM schemes in different signal domains

pointing out the main advantages and disadvantages of each scheme

and also pointing out the challenges faced by CIM. We believe that with the cross level collaborative research and development of process integration

devices

circuits

architecture

and software toolchains

CIM will provide more powerful and efficient computing power for AI computing at the edge and cloud ends.

关键词

Keywords

references

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