Physical Reservoir Computing Based on Nanoscale Materials and Devices

Zhiying Qi, Linjie Mi, Haoran Qian, Weiguo Zheng, Yao Guo*, Yang Chai*

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

16 Citations (Scopus)

Abstract

Bioinspired computation systems can achieve artificial intelligence, bypassing fundamental bottlenecks and cost constraints. Computational frameworks suited for temporal/sequential data processing such as recurrent neural networks (RNNs) suffer from problems of high complexity and low efficiency. Physical systems assembled with nanoscale materials and devices represent as an alternative route to serve as the core component for physically implanted reservoir computing. In this review, an overview of the development of the paradigm of physical reservoir computing (PRC) is provided and the typical physical reservoirs constructed with nanomaterials and nanodevices are described. The physical reservoirs based on multiple nanomaterials overcome the problems of RNN, show strong robustness, and effectively deal with tasks with improved reliability and availability. Finally, the challenges and perspectives of nanomaterial and nanodevice-based PRC as a component of next-generation machine learning systems are discussed.

Original languageEnglish
Article number2306149
JournalAdvanced Functional Materials
Volume33
Issue number43
DOIs
Publication statusPublished - 18 Oct 2023

Keywords

  • bioinspired computing
  • nanoelectronics
  • nanomaterials
  • neuromorphic computing
  • reservoir computing

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