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.