Versatile and Robust Reservoir Computing with PWM‐Driven Heterogenous <i>R</i> – <i>C</i> Circuits

Physical reservoir computing (PRC) holds great promise for low-latency, energy-efficient information processing, yet current implementations often suffer from limited flexibility, adaptability, and environmental stability. Here, a PRC system based on pulse-width modulation (PWM)-encoded resistor-capacitor (R-C) circuits is introduced, achieving exceptional versatility and robustness. By leveraging customizable nonlinearities and dynamic timescales, this system achieves state-of-the-art performance across diverse tasks, including chaotic time-series forecasting (NRMSE = 0.015 for Mackey-Glass) and complex multiscale tasks (94% accuracy for multiclass heartbeat classification). Notably, the design reduces relative errors by 98.4% across different device batches and under temperature variations compared to memristor-based reservoirs. These features position the approach as a scalable, adaptive, and energy-efficient solution for edge computing in dynamic environments, paving the way for robust and practical analog computing systems.

Authors

Institutions

Publication Details

Journal
Advanced Science
Published
2025-05-14
DOI
https://doi.org/10.1002/advs.202416413
Citations
2
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
4.96

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Versatile and Robust Reservoir Computing with PWM‐Driven Heterogenous R – C Circuits

Chang Cai, Bojun Cheng, Jun Ge, Weicheng Liu et al.
2 citations
Advanced Science
Neural Networks and Reservoir Computing
4.96
article

Versatile and Robust Reservoir Computing with PWM‐Driven Heterogenous R – C Circuits

Chang Cai, Bojun Cheng, Jun Ge, Weicheng Liu, Zelin Ma, Hemian Yi, Shangke Pan, Ziping Zheng, Zhenhang Chen, Yibing Chen
article en
2 citations

Abstract

Physical reservoir computing (PRC) holds great promise for low-latency, energy-efficient information processing, yet current implementations often suffer from limited flexibility, adaptability, and environmental stability. Here, a PRC system based on pulse-width modulation (PWM)-encoded resistor-capacitor (R-C) circuits is introduced, achieving exceptional versatility and robustness. By leveraging customizable nonlinearities and dynamic timescales, this system achieves state-of-the-art performance across diverse tasks, including chaotic time-series forecasting (NRMSE = 0.015 for Mackey-Glass) and complex multiscale tasks (94% accuracy for multiclass heartbeat classification). Notably, the design reduces relative errors by 98.4% across different device batches and under temperature variations compared to memristor-based reservoirs. These features position the approach as a scalable, adaptive, and energy-efficient solution for edge computing in dynamic environments, paving the way for robust and practical analog computing systems.

Advanced ScienceVol. 12(29)
Hong Kong University of Science and Technology (HK), Guangzhou University (CN), Department of Education of Guangdong Province (CN), University of Hong Kong (HK)
National Natural Science Foundation of China, Basic and Applied Basic Research Foundation of Guangdong Province
Affordable and clean energy
Openalex Percentile: Top 5%
Neural Networks and Reservoir Computing
4.96
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.