@inproceedings{
stern2023contrastive,
title={Contrastive power-efficient physical learning in resistor networks},
author={Menachem Stern and Sam Dillavou and Dinesh Jayaraman and Douglas Durian and Andrea Liu},
booktitle={Machine Learning with New Compute Paradigms},
year={2023},
url={https://openreview.net/forum?id=lTYd1pa8bO}
}
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.
Contrastive power-efficient physical learning in resistor networks
Menachem Stern Sam Dillavou Dinesh Jayaraman Douglas Durian Andrea Liu