All-optical ultrafast ReLU function for energy-efficient nanophotonic deep learning
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.
Cite
@article{arxiv.2201.03787,
title = {All-optical ultrafast ReLU function for energy-efficient nanophotonic deep learning},
author = {Gordon H. Y. Li and Ryoto Sekine and Rajveer Nehra and Robert M. Gray and Luis Ledezma and Qiushi Guo and Alireza Marandi},
journal= {arXiv preprint arXiv:2201.03787},
year = {2022}
}