Abstract
With the penetration of artificial intelligence (AI) technology into industrial applications, not only computational effectiveness but also computational efficiency in machine learning (ML) methods has been increasingly demanded. Reservoir computing (RC) is an ML framework leveraging a dynamic <i>reservoir</i> for a nonlinear transformation of sequential inputs and a <i>readout</i> for mapping the reservoir state to a desired output. Since only the readout is trained with a simple learning algorithm, RC has attracted much attention as a promising approach to enhance compatibility between high computational performance and low learning cost. In addition, recent studies on physical reservoirs implemented with various physical substrates have boosted the potential of RC in the development of effective and efficient AI hardware. Therefore, it is time to further explore the new frontiers in extremely efficient RC.
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