Rotating neurons for all-analog implementation of cyclic reservoir computing

Xiangpeng Liang, Yanan Zhong, Jianshi Tang (), Zhengwu Liu, Peng Yao, Keyang Sun, Qingtian Zhang, Bin Gao, Hadi Heidari (), He Qian and Huaqiang Wu ()
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Xiangpeng Liang: Tsinghua University
Yanan Zhong: Tsinghua University
Jianshi Tang: Tsinghua University
Zhengwu Liu: Tsinghua University
Peng Yao: Tsinghua University
Keyang Sun: Tsinghua University
Qingtian Zhang: Tsinghua University
Bin Gao: Tsinghua University
Hadi Heidari: University of Glasgow
He Qian: Tsinghua University
Huaqiang Wu: Tsinghua University

Nature Communications, 2022, vol. 13, issue 1, 1-11

Abstract: 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.

Date: 2022
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DOI: 10.1038/s41467-022-29260-1

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