Toward memristive in-memory computing: principles and applications

Han Bao; Houji Zhou; Jiancong Li; Huaizhi Pei; Jing Tian; Ling Yang; Shengguang Ren; Shaoqin Tong; Yi Li; Yuhui He; Jia Chen; Yimao Cai; Huaqiang Wu; Qi Liu; Qing Wan; Xiangshui Miao

doi:10.1007/s12200-022-00025-4

Toward memristive in-memory computing: principles and applications

Front Optoelectron. 2022 May 12;15(1):23. doi: 10.1007/s12200-022-00025-4.

Authors

Han Bao¹, Houji Zhou¹, Jiancong Li¹, Huaizhi Pei¹, Jing Tian¹, Ling Yang¹, Shengguang Ren¹, Shaoqin Tong¹, Yi Li^{2

3}, Yuhui He^{1

4}, Jia Chen⁵, Yimao Cai⁶, Huaqiang Wu⁷, Qi Liu⁸, Qing Wan⁹, Xiangshui Miao^{10

11}

Affiliations

¹ School of Integrated Circuits, School of Optical and Electronic Information, Wuhan National Laboratory for Optoelectronics, Optics Valley Laboratory, Huazhong University of Science and Technology, Wuhan, 430074, China.
² School of Integrated Circuits, School of Optical and Electronic Information, Wuhan National Laboratory for Optoelectronics, Optics Valley Laboratory, Huazhong University of Science and Technology, Wuhan, 430074, China. liyi@hust.edu.cn.
³ Hubei Yangtze Memory Laboratories, Wuhan, 430205, China. liyi@hust.edu.cn.
⁴ Hubei Yangtze Memory Laboratories, Wuhan, 430205, China.
⁵ AI Chip Center for Emerging Smart Systems, InnoHK Centers, Hong Kong Science Park, Hong Kong, China.
⁶ School of Integrated Circuits, Peking University, Beijing, 100871, China.
⁷ School of Integrated Circuits, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100084, China.
⁸ Frontier Institute of Chip and System, Fudan University, Shanghai, 200433, China.
⁹ School of Electronic Science and Engineering, and Collaborative Innovation Centre of Advanced Microstructures, Nanjing University, Nanjing, 210093, China.
¹⁰ School of Integrated Circuits, School of Optical and Electronic Information, Wuhan National Laboratory for Optoelectronics, Optics Valley Laboratory, Huazhong University of Science and Technology, Wuhan, 430074, China. miaoxs@hust.edu.cn.
¹¹ Hubei Yangtze Memory Laboratories, Wuhan, 430205, China. miaoxs@hust.edu.cn.

Abstract

With the rapid growth of computer science and big data, the traditional von Neumann architecture suffers the aggravating data communication costs due to the separated structure of the processing units and memories. Memristive in-memory computing paradigm is considered as a prominent candidate to address these issues, and plentiful applications have been demonstrated and verified. These applications can be broadly categorized into two major types: soft computing that can tolerant uncertain and imprecise results, and hard computing that emphasizes explicit and precise numerical results for each task, leading to different requirements on the computational accuracies and the corresponding hardware solutions. In this review, we conduct a thorough survey of the recent advances of memristive in-memory computing applications, both on the soft computing type that focuses on artificial neural networks and other machine learning algorithms, and the hard computing type that includes scientific computing and digital image processing. At the end of the review, we discuss the remaining challenges and future opportunities of memristive in-memory computing in the incoming Artificial Intelligence of Things era.

Keywords: Digital image processing; In-memory computing; Machine learning; Matrix–vector multiplication; Memristor; Scientific computing.

Publication types

Review