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28 September 2023 Online learning strategies for optical neural networks
Mirko Goldmann, Anas Skalli, Daniel Brunner
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Proceedings Volume PC12655, Emerging Topics in Artificial Intelligence (ETAI) 2023; PC1265514 (2023) https://doi.org/10.1117/12.2677449
Event: SPIE Nanoscience + Engineering, 2023, San Diego, California, United States
Abstract
Photonic platforms for neuromorphic computing promise high-speed and low-energy computations for machine learning. However, current learning schemes in optical systems are often limited to training only a linear output layer. Here, we discuss performance gains by training input and/or internal weights of neural networks for classification tasks. We focus on optimization methods that can be directly applied to physical hardware without the need for mathematical models of the hardware or measurement of the network's state. Accordingly, we target online learning strategies that increase computational capabilities beyond reservoir computing, paving the way to more autonomous and performant photonic hardware.
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Mirko Goldmann, Anas Skalli, and Daniel Brunner "Online learning strategies for optical neural networks", Proc. SPIE PC12655, Emerging Topics in Artificial Intelligence (ETAI) 2023, PC1265514 (28 September 2023); https://doi.org/10.1117/12.2677449
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KEYWORDS
Machine learning
Neural networks
Education and training
Online learning
Evolutionary algorithms
Computer hardware
Genetic algorithms
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