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28 September 2023 Nonlinear spatiotemporal optical computing
Ugur Tegin, Mustafa Yildirim, Ilker Oguz, Christophe Moser, Demetri Psaltis
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Proceedings Volume PC12655, Emerging Topics in Artificial Intelligence (ETAI) 2023; PC126550G (2023) https://doi.org/10.1117/12.2677238
Event: SPIE Nanoscience + Engineering, 2023, San Diego, California, United States
Abstract
In the field of machine learning, large datasets are essential for heavy tasks. However, the performance of power-hungry processors is limited by the data transfer to and from memory. Optical computing has been gaining interest as a means of high-speed computation, and here we present an optical computing framework called scalable optical learning operator based on spatiotemporal effects in multimode fibers. This framework is capable of performing various learning tasks, such as classifying COVID-19 X-ray lung images, speech recognition, and age prediction from face images. Our approach addresses the energy scaling problem without compromising speed by leveraging the simultaneous, linear and nonlinear interaction of spatial modes as a computation engine. Our experiments demonstrate the accuracy of our method comparable to a digital implementation.
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Ugur Tegin, Mustafa Yildirim, Ilker Oguz, Christophe Moser, and Demetri Psaltis "Nonlinear spatiotemporal optical computing", Proc. SPIE PC12655, Emerging Topics in Artificial Intelligence (ETAI) 2023, PC126550G (28 September 2023); https://doi.org/10.1117/12.2677238
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KEYWORDS
Nonlinear optics
Optical computing
Machine learning
Spatial light modulators
Interfaces
Lung
Multimode fibers
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