Delay-based reservoir computing using Mackey–Glass oscillator and Arduino board for edge intelligence applications

Abstract

This article introduces the experimental demonstration of the Mackey–Glass oscillator (MGO)/Arduino-based reservoir computing system as a novel versatile platform for several applications. Performance evaluations conducted on benchmark prediction tasks demonstrate the system’s capabilities with exceptional normalized mean square error (NMSE) values of up to 0.050 [log10(NMSE) ≃ −1.29] for Santa Fe and 0.0034 [log10(NMSE) ≃ −2.46] for electrocardiogram tasks. In addition, we achieve outstanding classification accuracy of up to 96.67% in the chaos recognition task. Our MGO/Arduino-based reservoir computing approach offers many advantages, such as cheapness, affordability, accessibility, and versatility, positioning it as a valuable and efficient solution in advancing neuromorphic computing for edge intelligence applications.