Energy-efficient time series processing in real-time with fluidic iontronic memristor circuits

TL;DR

This paper demonstrates that iontronic memristor circuits can perform real-time time series prediction with performance comparable to traditional methods but with significantly lower energy consumption, advancing ultra-low-power neuromorphic computing.

Contribution

It introduces a novel iontronic circuit model for time series tasks, enabling direct comparison with conventional reservoirs and showcasing ultra-low energy efficiency.

Findings

Prediction performance comparable to solid-state reservoirs

Energy consumption over 5 orders of magnitude lower

Open-source pyontronics package implementation

Abstract

Iontronic neuromorphic computing has emerged as a rapidly expanding paradigm. The arrival of angstrom-confined iontronic devices enables ultra-low power consumption with dynamics and memory timescales that intrinsically align well with signals of natural origin, a challenging combination for conventional (solid-state) neuromorphic materials. However, comparisons to earlier conventional substrates and evaluations of concrete application domains remain a challenge for iontronics. Here we propose a pathway toward iontronic circuits that can address established time series benchmark tasks, enabling performance comparisons and highlighting possible application domains for efficient real-time time series processing. We model a Kirchhoff-governed circuit with iontronic memristors as edges, while the dynamic internal voltages serve as output vector for a linear readout function, during which energy consumption is also logged. All these aspects are integrated into the open-source pyontronics package. Without requiring input encoding or virtual timing mechanisms, our simulations demonstrate prediction performance comparable to various earlier solid-state reservoirs, notably with an exceptionally low energy consumption of over 5 orders of magnitude lower. These results suggest a pathway of iontronic technologies for ultra-low-power real-time neuromorphic computation.