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

Li, Gordon H. Y. and Sekine, Ryoto and Nehra, Rajveer and Gray, Robert M. and Ledezma, Luis and Guo, Qiushi and Marandi, Alireza (2022) All-optical ultrafast ReLU function for energy-efficient nanophotonic deep learning. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20220224-200850779

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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.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
http://arxiv.org/abs/2201.03787arXivDiscussion Paper
ORCID:
AuthorORCID
Nehra, Rajveer0000-0002-0165-2188
Ledezma, Luis0000-0002-0365-1672
Guo, Qiushi0000-0002-6217-102X
Marandi, Alireza0000-0002-0470-0050
Additional Information:Attribution 4.0 International (CC BY 4.0). The device nanofabrication was performed at the Kavli Nanoscience Institute (KNI) at Caltech. The authors gratefully acknowledge support from ARO grant no. W911NF-18-1-0285, NSF grant no. 1846273 and 1918549, AFOSR award FA9550-20-1-0040, and NASA/JPL. The authors wish to thank NTT Research for their financial and technical support. Data availability. Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request. Disclosures. GHYL, RS, RN, RMG, AM: California Institute of Technology (P)
Group:Kavli Nanoscience Institute
Funders:
Funding AgencyGrant Number
Army Research Office (ARO)W911NF-18-1-0285
NSFECCS-1846273
NSFCCF-1918549
Air Force Office of Scientific Research (AFOSR)FA9550-20-1-0040
NASA/JPL/CaltechUNSPECIFIED
NTT ResearchUNSPECIFIED
Record Number:CaltechAUTHORS:20220224-200850779
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20220224-200850779
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:113591
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:28 Feb 2022 17:33
Last Modified:28 Feb 2022 17:33

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