Abstract
AbstractReservoir computing (RC) is a promising paradigm for machine learning that uses a fixed, randomly generated network, known as the reservoir, to process input data. A memristor with fading memory and nonlinearity characteristics was adopted as a physical reservoir to implement the hardware RC system. This article reviews the device requirements for effective memristive reservoir implementation and methods for obtaining higher‐dimensional reservoirs for improving RC system performance. In addition, recent in‐sensor RC system studies, which use a memristor that the resistance is changed by an optical signal to realize an energy‐efficient machine vision, are discussed. Finally, the limitations that the memristive and in‐sensor RC systems encounter when attempting to improve performance further are discussed, and future directions that may overcome these challenges are suggested.
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