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

Sowjanya Tammali; Wilkie Olin-Ammentorp

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arXiv · 2610.07283

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

Sowjanya Tammali·Wilkie Olin-Ammentorp

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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.

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BibTeX RIS

Sowjanya Tammali, Wilkie Olin-Ammentorp. 2026-10-05. Lock-in EP: An In-Situ Training Algorithm for Oscillatory Hardware. https://arxiv.org/abs/2610.07283

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