Energy-based neural network models with coherent laser networks

Mohammad-Ali Miri; Kevin Zelaya

doi:10.1117/12.2679117

4 October 2023 Energy-based neural network models with coherent laser networks

Mohammad-Ali Miri, Kevin Zelaya

Proceedings Volume PC12647, Active Photonic Platforms (APP) 2023; PC126470L (2023) https://doi.org/10.1117/12.2679117
Event: SPIE Nanoscience + Engineering, 2023, San Diego, California, United States

Abstract

We propose utilizing coherently coupled laser networks for neural computing. The proposed scheme is built on harnessing the collective behavior of laser networks for storing phase patterns as stable fixed points of the governing dynamical equations and retrieving such patterns through proper excitation conditions, thus exhibiting an associative memory property. We further show that limitations on the number of images can be overcome by using nonreciprocal coupling between lasers, thus allowing for utilizing the large storage capacity inherent to the laser network. This work opens new possibilities for neural computation with coherent laser networks as a novel physical analog processor.

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Mohammad-Ali Miri and Kevin Zelaya "Energy-based neural network models with coherent laser networks", Proc. SPIE PC12647, Active Photonic Platforms (APP) 2023, PC126470L (4 October 2023); https://doi.org/10.1117/12.2679117

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KEYWORDS

Physical coherence

Neural networks

Analog electronics

Content addressable memory

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