Publication date: 22nd July 2026
Physical neural networks typically train linear synaptic weights while treating device nonlinearities as fixed. We show the opposite - by training the synaptic nonlinearity itself using a Kolmogorov-Arnold framework, we yield markedly higher task performance per physical resource than conventional linear weight-based networks, and demonstrate that network topologies which directly learn nonlinear physical dynamics can enable strong learning at compact network sizes - up to 40-500x smaller than linear weight approaches. We demonstrate this in silicon-on-insulator on-chip devices operating at room temperature, 0.1-1 microampere currents, 750 fJ per nonlinear operation and 2 MHz speeds.
We show that physical KANs can be trained without reliance on backpropagation or surrogate device models, using forward-only noise-based decorrelated perturbative learning – which can match or exceed the performance of backpropagation.
Physical KANs demonstrate higher performance than equally-sized linear-weight based networks across a range of classification & regression tasks. These results establish programmable heterogenous physical nonlinearity as a promising computational primitive for compact and efficient learning systems.
Taglietti, Fabiana, et al. & Jack C. Gartside "Learning Nonlinear Heterogeneity in Physical Kolmogorov-Arnold Networks." arXiv preprint arXiv:2601.15340 (2026).
Liu, Ziming, et al. "Kan: Kolmogorov-arnold networks." arXiv preprint arXiv:2404.19756 (2024).
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