Low-power analogue neural networks with trainable nonlinear connections for continuous control
arXiv:2606.23742v1 Announce Type: new Abstract: Physical neural networks promise low-power machine learning by computing directly with analogue device physics, but most architectures force nonlinear device responses to act as scalar weights. Inspired by Kolmogorov-Arnold networks, we place trainable nonlinear functions on the connections, making each physical connection a learnable computational element. Realising these functions as analogue band-pass filters on field-programmable analogue arrays, we find that the benefit is task-dependent and follows from the smoothness of the physical basis: the networks represent smooth, continuously valued targets, including robotic kinematics, continuous control, and photovoltaic maximum-power-point tracking, with far fewer nodes and connections than multilayer perceptrons, but offer no parameter-efficiency advantage on classification-like decision boundaries. Trained networks transfer to hardware across approximately 35,000 connections with quantified fidelity, and a dedicated CMOS implementation is projected to operate at approximately 30 microwatts. A memristive realisation reproduces the same behaviour in simulation, indicating that the advantage comes from placing trainable nonlinearity on connections, rather than from a particular device.
[2606.23742] Low-power analogue neural networks with trainable nonlinear connections for continuous control
[Submitted on 21 Jun 2026]
Title:Low-power analogue neural networks with trainable nonlinear connections for continuous control
View a PDF of the paper titled Low-power analogue neural networks with trainable nonlinear connections for continuous control, by Ian T. Vidamour and 14 other authors
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Abstract:Physical neural networks promise low-power machine learning by computing directly with analogue device physics, but most architectures force nonlinear device responses to act as scalar weights. Inspired by Kolmogorov-Arnold networks, we place trainable nonlinear functions on the connections, making each physical connection a learnable computational element. Realising these functions as analogue band-pass filters on field-programmable analogue arrays, we find that the benefit is task-dependent and follows from the smoothness of the physical basis: the networks represent smooth, continuously valued targets, including robotic kinematics, continuous control, and photovoltaic maximum-power-point tracking, with far fewer nodes and connections than multilayer perceptrons, but offer no parameter-efficiency advantage on classification-like decision boundaries. Trained networks transfer to hardware across approximately 35,000 connections with quantified fidelity, and a dedicated CMOS implementation is projected to operate at approximately 30 microwatts. A memristive realisation reproduces the same behaviour in simulation, indicating that the advantage comes from placing trainable nonlinearity on connections, rather than from a particular device.
Comments: Preprint. Further verification of all simulations is ongoing. Any resulting corrections will be incorporated in a revised version
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR)
Cite as: arXiv:2606.23742 [cs.LG]
(or arXiv:2606.23742v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.23742
arXiv-issued DOI via DataCite
Submission history
From: Eleni Vasilaki D.Phil. [view email] [v1] Sun, 21 Jun 2026 15:55:23 UTC (13,966 KB)
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