Low-power-consumption physical reservoir computing model based on overdamped bistable stochastic resonance system

Abstract

The physical implementation of reservoir computing (RC), a brain-inspired computing framework, is attracting increasing attention in various research fields owing to its capability of quick learning and the relatively simple training process. Although several physical RC models have been envisaged and realized, most of them require additional peripherals; it leads to an increase in the power consumption of the system. In this study, we propose a novel RC model that is based on an overdamped bistable system and exhibits a counter-intuitive phenomenon called stochastic resonance, through which it can transfer noise energy to the information-carrying signal to realize learning with a comparatively low power consumption. The proposed model also possesses the functional capability of filtering and amplification and thus does not require additional peripheral equipment. In order to prove the feasibility and determine the desired operation mode of the system, two basic benchmark tests, namely short-term memory and parity check tasks, were employed to assess the proposed model. The results verify that this work will potentially act as a stepping stone towards realizing even better low-power-consumption physical RC.