Physical reservoir computing -- An introductory perspective
It addresses the problem of reducing data transmission overhead in decentralized edge computing for applications like sensors, though it is an introductory perspective rather than a novel advancement.
The paper introduces physical reservoir computing as a framework to exploit complex dynamics of physical systems for information processing, particularly in edge computing devices to reduce adaptation delays, and illustrates its potential with examples from soft robotics.
Understanding the fundamental relationships between physics and its information-processing capability has been an active research topic for many years. Physical reservoir computing is a recently introduced framework that allows one to exploit the complex dynamics of physical systems as information-processing devices. This framework is particularly suited for edge computing devices, in which information processing is incorporated at the edge (e.g., into sensors) in a decentralized manner to reduce the adaptation delay caused by data transmission overhead. This paper aims to illustrate the potentials of the framework using examples from soft robotics and to provide a concise overview focusing on the basic motivations for introducing it, which stem from a number of fields, including machine learning, nonlinear dynamical systems, biological science, materials science, and physics.