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

Kavli Affiliate: Alireza Marandi

| First 5 Authors: Gordon H. Y. Li, Ryoto Sekine, Rajveer Nehra, Robert M. Gray, Luis Ledezma

| Summary:

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.

| Search Query: ArXiv Query: search_query=au:”Alireza Marandi”&id_list=&start=0&max_results=10

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