Emerging memristors and applications in reservoir computing

Abstract

Recently, with the emergence of ChatGPT, the field of artificial intelligence has garnered widespread attention from various sectors of society. Reservoir 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 physical 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 robustness. Finally, we discussed the development prospects and challenges faced by memristors-based RC systems.

Graphical abstract

Keywords

reservoir computing / memristor / resistive switching / artificial synapse / neuromorphic computing

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Declarations

The authors declare that they have no competing interests and there are no conflicts.

Acknowledgements

This work was financially supported by the National Natural Science Foundation of China (Grant Nos. 11574057 and 12172093) and the Guangdong Basic and Applied Basic Research Foundation (Grant No. 2021A1515012607).

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