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18 June 2024 Optoacoustic recurrent operator
Steven Becker, Dirk Englund, Birgit Stiller
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Proceedings Volume PC13017, Machine Learning in Photonics; PC1301704 (2024) https://doi.org/10.1117/12.3016223
Event: SPIE Photonics Europe, 2024, Strasbourg, France
Abstract
We experimentally demonstrate an optoacoustic recurrent operator (OREO) based on stimulated Brillouin scattering, which enables recurrent functionalities for photonic machine learning and neural network applications. OREO employs sound waves to catch and process the context defined by a sequence of optical pulses. It controls the coherent recurrent operation completely optically on pulse-by-pulse level without the need of an artificial reservoir. We demonstrate OREO's capability to compute correlations in pulse trains. Then, we use the pulse-by-pulse control of OREO to implement recurrent dropout. Furthermore, we use OREO to recognize patterns of optical pulse trains, in which we can distinguish up-to 27 different patterns. Eventually, OREO can be used as key component of a bi-directional perceptron, bring a new class of photonic neural networks within reach.
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Steven Becker, Dirk Englund, and Birgit Stiller "Optoacoustic recurrent operator", Proc. SPIE PC13017, Machine Learning in Photonics, PC1301704 (18 June 2024); https://doi.org/10.1117/12.3016223
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KEYWORDS
Optoacoustics
Artificial neural networks
Education and training
Control systems
Machine learning
Neural networks
Optical coherence
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