In plain words: A tiny patterned-crystal waveguide performs ReLU — the switch-like step that lets a neural network learn — entirely with light, so signals never need converting to electricity. It needs only femtojoules of energy per activation and works nearly instantly, unlike earlier optical nonlinear steps.
Abstract · All-optical ultrafast ReLU function for energy-efficient nanophotonic deep learning
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.
Gordon H. Y. Li, Ryoto Sekine, Rajveer Nehra, Robert M. Gray, Luis Ledezma, Qiushi Guo, Alireza Marandi
arXiv:2201.03787 · physics.optics, cs.ET · submitted Jan 11, 2022
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