Skip to yearly menu bar Skip to main content

Workshop: Machine Learning with New Compute Paradigms

Contrastive power-efficient physical learning in resistor networks

Menachem Stern · Sam Dillavou · Dinesh Jayaraman · Douglas Durian · Andrea Liu

[ ] [ Project Page ]
Sat 16 Dec 9:25 a.m. PST — 10:30 a.m. PST


The prospect of substantial reductions in the power consumption of AI is a major motivation for the development of neuromorphic hardware. Less attention has been given to the complementary research of power-efficient learning rules for such systems. Here we study self-learning physical systems trained by local learning rules based on contrastive learning. We show how the physical learning rule can be biased toward finding power-efficient solutions to learning problems, and demonstrate in simulations and laboratory experiments the emergence of a trade-off between power-efficiency and task performance.

Chat is not available.