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Front. Optoelectron.    2022, Vol. 15 Issue (2) : 23    https://doi.org/10.1007/s12200-022-00025-4
REVIEW ARTICLE
Toward memristive in-memory computing: principles and applications
Han Bao1(), Houji Zhou1, Jiancong Li1, Huaizhi Pei1, Jing Tian1, Ling Yang1, Shengguang Ren1, Shaoqin Tong1, Yi Li1,2(), Yuhui He1,2, Jia Chen3, Yimao Cai4, Huaqiang Wu5, Qi Liu6, Qing Wan7, Xiangshui Miao1,2()
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. Hubei Yangtze Memory Laboratories, Wuhan 430205, China
3. AI Chip Center for Emerging Smart Systems, InnoHK Centers, Hong Kong Science Park, Hong Kong, China
4. School of Integrated Circuits, Peking University, Beijing 100871, China
5. School of Integrated Circuits, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
6. Frontier Institute of Chip and System, Fudan University, Shanghai 200433, China
7. School of Electronic Science and Engineering, and Collaborative Innovation Centre of Advanced Microstructures, Nanjing University, Nanjing 210093, China
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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 Memristor      In-memory computing      Matrix–vector multiplication      Machine learning      Scientific computing      Digital image processing     
Corresponding Author(s): Han Bao,Yi Li,Xiangshui Miao   
Issue Date: 22 June 2022
 Cite this article:   
Han Bao,Houji Zhou,Jiancong Li, et al. Toward memristive in-memory computing: principles and applications[J]. Front. Optoelectron., 2022, 15(2): 23.
 URL:  
https://academic.hep.com.cn/foe/EN/10.1007/s12200-022-00025-4
https://academic.hep.com.cn/foe/EN/Y2022/V15/I2/23
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