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arXiv · 2201.03787

All-optical ultrafast ReLU function for energy-efficient nanophotonic deep learning

Gordon H. Y. Li·Ryoto Sekine·Rajveer Nehra·Robert M. Gray·Luis Ledezma·Qiushi Guo·Alireza Marandi
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Abstract

In recent years, the computational demands of deep learning applications have necessitated the introduction of energy-efficient hardware accelerators. Optical neural networks are a promising option; however, thus far they have been largely limited by the lack of energy-efficient nonlinear optical functions. Here, we experimentally demonstrate an all-optical Rectified Linear Unit (ReLU), which is the most widely used nonlinear activation function for deep learning, using a periodically-poled thin-film lithium niobate nanophotonic waveguide and achieve ultra-low energies in the regime of femtojoules per activation with near-instantaneous operation. Our results provide a clear and practical path towards truly all-optical, energy-efficient nanophotonic deep learning.

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Gordon H. Y. Li, Ryoto Sekine, Rajveer Nehra, Robert M. Gray, Luis Ledezma, Qiushi Guo, Alireza Marandi. 2022-01-11. All-optical ultrafast ReLU function for energy-efficient nanophotonic deep learning. https://arxiv.org/abs/2201.03787

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