Neuromorphic Electronic Systems for Reservoir Computing

TL;DR

This survey reviews neuromorphic hardware implementations of reservoir computing, highlighting progress, technical challenges, and potential solutions for efficient computation on unconventional substrates.

Contribution

It provides a comprehensive overview of experimental studies on neuromorphic reservoir computing and discusses technical challenges and potential solutions.

Findings

Multiple hardware implementations of reservoir computing have been developed.

Training remains simple due to linear regression, enabling efficient hardware deployment.

The survey identifies key technical challenges and suggests future research directions.

Abstract

This chapter provides a comprehensive survey of the researches and motivations for hardware implementation of reservoir computing (RC) on neuromorphic electronic systems. Due to its computational efficiency and the fact that training amounts to a simple linear regression, both spiking and non-spiking implementations of reservoir computing on neuromorphic hardware have been developed. Here, a review of these experimental studies is provided to illustrate the progress in this area and to address the technical challenges which arise from this specific hardware implementation. Moreover, to deal with challenges of computation on such unconventional substrates, several lines of potential solutions are presented based on advances in other computational approaches in machine learning. Keywords: Analog Microchips, FPGA, Memristors, Neuromorphic Architectures, Reservoir Computing