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 (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20220224-200850779
![]() |
PDF
- Submitted Version
Creative Commons Attribution. 1MB |
Use this Persistent URL to link to this item: https://resolver.caltech.edu/CaltechAUTHORS:20220224-200850779
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: |
| ||||||||||||||
ORCID: |
| ||||||||||||||
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: |
| ||||||||||||||
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 |
Repository Staff Only: item control page