Two Orders of Magnitude Reduction in Computational Load Achieved by Ultrawideband Responses of an Ion-Gating Reservoir
Daiki Nishioka, Hina Kitano, Wataru Namiki, Satofumi Souma, Kazuya Terabe, Takashi Tsuchiya
Abstract
The rising energy demands of conventional AI systems underscore the need for efficient computing technologies, such as brain-inspired computing. Physical reservoir computing (PRC), leveraging the nonlinear dynamics of physical systems for information processing, has emerged as a promising approach for neuromorphic computing. However, current PRC systems are constrained by narrow responsive time scales and limited performance. To address these challenges, an ion-gel/graphene electric double layer (EDL) transistor-based ion-gating reservoir (IGR) was developed. This IGR achieves a highly tunable and ultrawide time-scale response through the coexistence of fast EDL dynamics at the ion-gel/graphene interface and slower molecular adsorption dynamics on the graphene surface. Consequently, the system demonstrates an exceptionally broad responsive range, from 1 MHz to 20 Hz, while maintaining a high information processing capacity and adaptability across multiple time scales. The IGR achieved deep learning (DL)-level accuracy in chaotic time series prediction tasks while reducing computational resource requirements to 1/100 of those needed by DL. Principal component analysis reveals the IGR's superior performance stems from its high-dimensionality, driven by the ultrawideband responses of the EDL along with the ambipolar behavior of graphene. The proposed IGR represents a significant step forward in providing low-power, high-performance computing solutions, particularly for resource-constrained edge environments.