首页|Emerging memristors and applications in reservoir computing

Emerging memristors and applications in reservoir computing

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Recently,with the emergence of ChatGPT,the field of artificial intelligence has garnered widespread attention from various sectors of society.Reser-voir Computing(RC)is a neuromorphic computing algorithm used to analyze time-series data.Unlike traditional artificial neural networks that require the weight values of all nodes in the trained network,RC only needs to train the readout layer.This makes the training process faster and more efficient,and it has been used in various applications,including speech recognition,image classification,and control systems.Its flexibility and efficiency make it a popular choice for processing large amounts of complex data.A recent research trend is to develop physical RC,which utilizes the nonlinear dynamic and short-term memory properties of phys-ical systems(photonic modules,spintronic devices,memristors,etc.)to construct a fixed random neural network structure for processing input data to reduce computing time and energy.In this paper,we introduced the recent development of memristors and demonstrated the remarkable data processing capability of RC systems based on memristors.Not only do they possess excellent data processing ability comparable to digital RC systems,but they also have lower energy consumption and greater robust-ness.Finally,we discussed the development prospects and challenges faced by memristors-based RC systems.

reservoir computingmemristorresistive switchingartificial synapseneuromorphic computing

Hao Chen、Xin-Gui Tang、Zhihao Shen、Wen-Tao Guo、Qi-Jun Sun、Zhenhua Tang、Yan-Ping Jiang

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School of Physics and Optoelectronic Engineering,Guangdong University of Technology,Guangzhou Higher Education Mega Center,Guangzhou 510006,China

National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaGuangdong Basic and Applied Basic Research Foundation

11574057121720932021A1515012607

2024

物理学前沿
高等教育出版社

物理学前沿

CSTPCD
影响因子：0.816
ISSN：2095-0462
年,卷(期)：2024.19(1)
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