Dynamic memristor-based reservoir computing for high-efficiency temporal signal processing

• Published in: Nature communications Vol. 12; no. 1; p. 408
• Main Authors: Zhong, Yanan, Tang, Jianshi, Li, Xinyi, Gao, Bin, Qian, He, Wu, Huaqiang
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 18.01.2021; Nature Publishing Group; Nature Portfolio
• Subjects: 639/925/927/1007; 639/925/929/115; Computing time; Efficiency; Errors; Hardware; Humanities and Social Sciences; Mean square errors; Memristors; multidisciplinary; Neural networks; Neurons; Parameters; Predictions; Recognition; Recurrent neural networks; Science; Science (multidisciplinary); Signal processing; Task complexity; Time series
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-020-20692-1

Reservoir computing is a highly efficient network for processing temporal signals due to its low training cost compared to standard recurrent neural networks, and generating rich reservoir states is critical in the hardware implementation. In this work, we report a parallel dynamic memristor-based reservoir computing system by applying a controllable mask process, in which the critical parameters, including state richness, feedback strength and input scaling, can be tuned by changing the mask length and the range of input signal. Our system achieves a low word error rate of 0.4% in the spoken-digit recognition and low normalized root mean square error of 0.046 in the time-series prediction of the Hénon map, which outperforms most existing hardware-based reservoir computing systems and also software-based one in the Hénon map prediction task. Our work could pave the road towards high-efficiency memristor-based reservoir computing systems to handle more complex temporal tasks in the future. Designing efficient neuromorphic systems for complex temporal tasks remains a challenge. Zhong et al. develop a parallel memristor-based reservoir computing system capable of tuning critical parameters, achieving classification accuracy of 99.6% in spoken-digit recognition and time-series prediction error of 0.046 in the Hénon map.