Rotating neurons for all-analog implementation of cyclic reservoir computing

Liang, X. et al. (2022) Rotating neurons for all-analog implementation of cyclic reservoir computing. Nature Communications, 13, 1549. (doi: 10.1038/s41467-022-29260-1)

Abstract

Hardware implementation in resource-efficient reservoir computing is of great interest for neuromorphic engineering. Recently, various devices have been explored to implement hardware-based reservoirs. However, most studies were mainly focused on the reservoir layer, whereas an end-to-end reservoir architecture has yet to be developed. Here, we propose a versatile method for implementing cyclic reservoirs using rotating elements integrated with signal-driven dynamic neurons, whose equivalence to standard cyclic reservoir algorithm is mathematically proven. Simulations show that the rotating neuron reservoir achieves record-low errors in a nonlinear system approximation benchmark. Furthermore, a hardware prototype was developed for near-sensor computing, chaotic time-series prediction and handwriting classification. By integrating a memristor array as a fully-connected output layer, the all-analog reservoir computing system achieves 94.0% accuracy, while simulation shows >1000× lower system-level power than prior works. Therefore, our work demonstrates an elegant rotation-based architecture that explores hardware physics as computational resources for high-performance reservoir computing.

• Item Type: Articles
• Additional Information: his work was in part supported by China’s key research and development program 2021ZD0201205 (H.W.), Natural Science Foundation of China 91964104 (J.T.), 61974081 (J.T.), 62025111 (H.W.), 62104126 (Y.Z.), XPLORER Prize (H.W.), 92064001 (B.G.) and the UK EPSRC under grant EP/W522168/1 (H.H.).
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Heidari, Professor Hadi and Liang, Xiangpeng
• Authors: Liang, X., Zhong, Y., Tang, J., Liu, Z., Yao, P., Sun, K., Zhang, Q., Gao, B., Heidari, H., Qian, H., and Wu, H.
• College/School: College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
• Journal Name: Nature Communications
• Publisher: Nature Research
• ISSN: 2041-1723
• ISSN (Online): 2041-1723
• Copyright Holders: Copyright © The Author(s) 2022
• First Published: First published in Nature Communications 13:1549
• Publisher Policy: Reproduced under a Creative Commons Licence