Lock-in EP: An In-Situ Training Algorithm for Oscillatory Hardware

Abstract:Analog hardware platforms offer the potential to reduce energy consumption over digital architectures, but in order to succeed, large-scale analog systems must also be able to operate with or recover from the variability of their components. Towards this goal, we derive and demonstrate the lock-in equilibrium propagation (LIEP) training method. LIEP provides local gradient information for each component in an oscillatory network without separate forward and backward sweeps, potentially allowing for in-situ learning capabilities on analog oscillatory hardware platforms. We demonstrate that LIEP can be used both for ab-initio training as well as recovering performance when pre-trained parameters are perturbed. We show that LIEP can be formulated as a three-factor update rule, and suggest that although the method is currently only validated on shallow networks, alternate architectures may allow it to extend to deep and large-scale networks addressing complex tasks.

Machine Learning (cs.LG)

arXiv:2610.07283 [cs.LG]

(or arXiv:2610.07283v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.07283 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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