Physical Reservoir Computing Based on Nanoscale Materials and Devices

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.

Automated summary

This review provides an overview of the development of physical reservoir computing (PRC) and describes typical physical reservoirs constructed with nanomaterials and nanodevices. The physical reservoirs based on multiple nanomaterials overcome the problems of high complexity and low efficiency in recurrent neural networks (RNNs) and show strong robustness, effectively dealing with tasks with improved reliability and availability. The challenges and perspectives of nanomaterial and nanodevice-based PRC as a component of next-generation machine learning systems are also discussed.