Effective simulation of in-materio physical reservoirs: A unidirectional cluster-based echo state network approach

Abstract

Fabricated in-materio reservoir computing devices have demonstrated ultra-low power consumption for unconventional computing tasks across varieties of material systems. Simulating their internal networks is desired to analyze them, but challenging due to physical variability. While echo state networks (ESNs) provide a relevant framework, conventional ESN simulations require a long computational time at large scales and fail to replicate physical device outputs. We propose a novel ESN framework incorporating structural constraints inspired by physical in-materio devices, featuring a unidirectional cluster-based topology. Our approach demonstrates high consistency, faster computation, and promising benchmark performance compared to conventional ESNs.