Neuromorphic Electronic Systems for Reservoir Computing
It provides a review for researchers interested in efficient neuromorphic computing, but is incremental as it summarizes existing studies without new results.
This paper surveys hardware implementations of reservoir computing on neuromorphic electronic systems, highlighting their computational efficiency and simple linear regression training, while addressing technical challenges and potential solutions from machine learning advances.
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