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

TL;DR

Problem: real-time time-series processing with ultra-low power remains challenging; Approach: a Kirchhoff-governed fluidic circuit of iontronic memristors (SVMs) with a linear readout; Results: on Mackey-Glass benchmarks, $\mathrm{NRMSE}_1 \approx 0.059$ for 1-step ahead and $\mathrm{NRMSE}_1 \approx 0.043$ with three parallel networks, with total power around $\sim 16$ pW; Significance: demonstrates potential for multi-ms to s timescale processing using angstrom-scale channels, and the open-source pyontronics toolkit enables benchmarking and design exploration.

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.

Figures (2)

• Figure 1: (a) Schematic diagram of the modelled system, with a fluidic circuit consisting of iontronic memristors connected by aqueous reservoirs. A time series input is directly applied as imposed voltages at one or multiple of the reservoir nodes (green), with at least one ground (red). The free voltages in the remaining $N_{\text{free}}$ nodes (grey) evolve according to Kirchhoff's law and the dynamically evolving memristor conductances. These voltages, concatenated with constant 1 V offset voltage such that $\mathbf{V}_{\text{free}}(t)=(V_1,..,V_{N_{\text{free}}}, 1)$ V, are directly copied as voltage inputs into a linear readout function such that the output is $\textbf{y}(t)=W_{\text{out}}\mathbf{V}_{\text{free}}$. The physical implementation of such a matrix-vector multiplication is a standard crossbar array as depicted on the right. (b) Photograph of the full device that features a membrane connecting two aqueous electrolyte reservoirs. The membrane features a collection on conical channels that can conduct an ionic current. (c) Voltage-dependent steady state conductance as measured from the 12 $\mu\text{m}$ thick membrane. The inset shows the corresponding current-voltage relation from the experiment (blue) and a theoretical approximation (red) Boon2022Pressure-sensitiveGeometry. (d) Dynamic $IV$ measurements (blue graphs) of membranes of thicknesses 2.5 $\mu\text{m}$ (left) and 12 $\mu\text{m}$ (right), revealing memristive hysteresis loops that emerge across different frequencies. In red are the dynamic currents as predicted by the SVM theory as in Eq. (\ref{['eq:dgdt']}) with timescales $\tau$ of 1 ms and 10 ms for the short and long channels respectively.
• Figure 2: (a) Mackey-Glass series (blue) and the predictions for 1 step ahead from a physical fluidic circuit containing 15 iontronic memristors (red), with step size 1 ms. This yielded a $\text{NRMSE}_{1}\approx0.059$, averaged over 4900 steps on a test series. (b) Schematic depiction of the fluidic circuit with green and red the input and ground nodes respectively, while the remaining grey nodes represent free voltages. (c) Total power usage of the fluidic circuit at any given time (orange) and on average (black), using the estimate of an individual channel equilibrium conductance of 1 pS. (d) Comparisons of the power consumption of the reservoir circuit compared to various literature results that report the same MG $\text{NRMSE}_{1}$ performance metric Liang2022RotatingComputingSun2023ExperimentalSystemLiu2023Interface-typeComputingWu2024ACuInP2S6Ma2025VersatileCircuits. The blue circle corresponds to the small 15 SVM network discussed in Figs. (a-c) and the orange circle represents the larger network of 3 parallel implementations of the circuit in (b) with $\text{NRMSE}_{1}\approx0.043$. A power consumption improvement of over 5 orders of magnitude is visible when compared to rotating neurons Liang2022RotatingComputing, skyrmion-enhanced strain-mediated devices (Skyrmion) Sun2023ExperimentalSystem, $\text{Hf}_{0.5}\text{Zr}_{0.5}\text{O}_2$ (HZO) transistors Liu2023Interface-typeComputing, $\text{CuInP}_2\text{S}_6$ (CIPS) memristors Wu2024ACuInP2S6, and pulse-width modulation resistor-capacitor (PWM RC) Ma2025VersatileCircuits. An increased performance is found for higher power consumption in the literature results (yellow oval), also considerably driven by the differing reservoir sizes. Going from 1 circuit of SVMs (blue circle) to 3 parallel implementations of this circuit (orange circle) also improves performance, but whether such a trend continues (blue oval) remains to be investigated.