Towards mixed physical node reservoir computing: light-emitting synaptic reservoir system with dual photoelectric output

Minrui Lian; Changsong Gao; Zhenyuan Lin; Liuting Shan; Cong Chen; Yi Zou; Enping Cheng; Changfei Liu; Tailiang Guo; Wei Chen; Huipeng Chen

doi:10.1038/s41377-024-01516-z

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Towards mixed physical node reservoir computing: light-emitting synaptic reservoir system with dual photoelectric output

Original Articles

- Towards mixed physical node reservoir computing: light-emitting synaptic reservoir system with dual photoelectric output
- Light: Science & Applications Vol. 13, Issue 9, Pages: 1865-1877(2024)
- Affiliations：
  
  1.Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou 350002, China
  2.Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou 350100, China
  3.Joint School of National University of Singapore and Tianjin University, International Campus of Tianjin University, Fuzhou 350207, China
  4.Department of Chemistry, National University of Singapore, 3 Science Drive 3, Singapore 117543, Singapore
  5.Department of Physics, National University of Singapore, 3 Science Drive 3, Singapore 117543, Singapore
- Author bio：
  
  Huipeng Chen (hpchen@fzu.edu.cn)
- Funds：
- DOI：10.1038/s41377-024-01516-z
  CLC：
- Published：30 September 2024，
  
  Published Online：01 August 2024，
  
  Received：25 January 2024，
  
  Revised：19 May 2024，
  
  Accepted：29 June 2024
- Accepted：
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Lian, M. R. et al. Towards mixed physical node reservoir computing: light-emitting synaptic reservoir system with dual photoelectric output. Light: Science & Applications, 13, 1865-1877 (2024).
DOI：

Lian, M. R. et al. Towards mixed physical node reservoir computing: light-emitting synaptic reservoir system with dual photoelectric output. Light: Science & Applications, 13, 1865-1877 (2024). DOI： 10.1038/s41377-024-01516-z.

摘要

Abstract

Memristor-based physical reservoir computing holds significant potential for efficiently processing complex spatiotemporal data

which is crucial for advancing artificial intelligence. However

owing to the single physical node mapping characteristic of traditional memristor reservoir computing

it inevitably induces high repeatability of eigenvalues to a certain extent and significantly limits the efficiency and performance of memristor-based reservoir computing for complex tasks. Hence

this work firstly reports an artificial light-emitting synaptic (LES) device with dual photoelectric output for reservoir computing

and a reservoir system with mixed physical nodes is proposed. The system effectively transforms the input signal into two eigenvalue outputs using a mixed physical node reservoir comprising distinct physical quantities

namely optical output with nonlinear optical effects and electrical output with memory characteristics. Unlike previously reported memristor-based reservoir systems

which pursue rich reservoir states in one physical dimension

our mixed physical node reservoir system can obtain reservoir states in two physical dimensions with one input without increasing the number and types of devices. The recognition rate of the artificial light-emitting synaptic reservoir system can achieve 97.22% in MNIST recognition. Furthermore

the recognition task of multichannel images can be realized through the nonlinear mapping of the photoelectric dual reservoir

resulting in a recognition accuracy of 99.25%. The mixed physical node reservoir computing proposed in this work is promising for implementing the development of photoelectric mixed neural networks and material-algorithm collaborative design.

关键词

Keywords

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