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13 March 2024 Optic neural networks using multiple scattering for linear and non-linear transformations
Mustafa Yildirim, Niyazi Ulas Dinc, Ilker Oguz, Demetri Psaltis, Christophe Moser
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Proceedings Volume PC12903, AI and Optical Data Sciences V; PC129030N (2024) https://doi.org/10.1117/12.3001620
Event: SPIE OPTO, 2024, San Francisco, California, United States
Abstract
Optical computing offers a promising solution for achieving high-performance neural networks without relying heavily on electronic computing power. This work introduces a novel framework for implementing programmable neural networks. The framework enables both linear and nonlinear transformations at low optical power by leveraging multiple scattering and data repetition to achieve high-order nonlinearities. By combining linear optics and structural nonlinearity, it offers scalability and programmability for optical computing, particularly in artificial intelligence applications. The framework's ability to synthesize a learnable linear and nonlinear data transform bridges the gap between optical and digital neural networks.
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Mustafa Yildirim, Niyazi Ulas Dinc, Ilker Oguz, Demetri Psaltis, and Christophe Moser "Optic neural networks using multiple scattering for linear and non-linear transformations", Proc. SPIE PC12903, AI and Optical Data Sciences V, PC129030N (13 March 2024); https://doi.org/10.1117/12.3001620
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KEYWORDS
Neural networks
Nonlinear optics
Multiple scattering
Data processing
Electronic components
Energy efficiency
Imperfections
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