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Overview of Memristor-Based Neural Network Design and Applications

Longcheng YeCollege of Integrated Circuit Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing, ChinaZhixuan GaoCollege of Integrated Circuit Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing, ChinaJinke FuCollege of Integrated Circuit Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing, ChinaWang RenShanghai Aerospace Electronic Technology Institute, Shanghai, ChinaCihui YangSchool of Information Engineering, Nanchang Hangkong University, Nanchang, ChinaJing WenSchool of Information Engineering, Nanchang Hangkong University, Nanchang, ChinaXiang WanCollege of Integrated Circuit Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing, ChinaQingying RenElectrical and Electronic Laboratory Center, College of Electrical and Optical Engineering and College of Microelectronics, Nanjing University of Post and Telecommunications, Nanjing, ChinaShi‐Pu GuCollege of Electronic and Optical Engineering, College of Microelectronics, Nanjing University of Posts and Telecommunications, Nanjing, ChinaXiaoyan LiuCollege of Integrated Circuit Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing, ChinaXiaojuan LianCollege of Integrated Circuit Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing, ChinaLei WangCollege of Integrated Circuit Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China

2022en

ABI

Annotatsiya

Conventional von Newmann-based computers face severe challenges in the processing and storage of the large quantities of data being generated in the current era of “big data.” One of the most promising solutions to this issue is the development of an artificial neural network (ANN) that can process and store data in a manner similar to that of the human brain. To extend the limits of Moore’s law, memristors, whose electrical and optical behaviors closely match the biological response of the human brain, have been implemented for ANNs in place of the traditional complementary metal-oxide-semiconductor (CMOS) components. Based on their different operation modes, we classify the memristor family into electronic, photonic, and optoelectronic memristors, and review their respective physical principles and state-of-the-art technologies. Subsequently, we discuss the design strategies, performance superiorities, and technical drawbacks of various memristors in relation to ANN applications, as well as the updated versions of ANN, such as deep neutral networks (DNNs) and spike neural networks (SNNs). This paper concludes by envisioning the potential approaches for overcoming the physical limitations of memristor-based neural networks and the outlook of memristor applications on emerging neural networks.

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DOI: 10.3389/fphy.2022.839243

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