Research Status of Computing-in-Memory Technology

LI Jia-ning; YAO Peng; JIE Lu; TANG Jian-shi; WU Dong; GAO Bin; QIAN He; WU Hua-qiang

doi:10.12263/DZXB.20230967

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Research Status of Computing-in-Memory Technology

Special Issue: Recipients of CIE Science and Technology Awards | 更新时间：2025-12-08

- Research Status of Computing-in-Memory Technology
- ACTA ELECTRONICA SINICA Vol. 52, Issue 4, Pages: 1103-1117(2024)
- 作者机构：
  
  清华大学集成电路学院，北京 100084
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(92164302;62025111)
- DOI：10.12263/DZXB.20230967
  CLC： TP389.1
- Received：19 October 2023，
  
  Revised：2024-04-03，
  
  Published：25 April 2024
- 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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