Hyper-multiplexed, Ultralow-Energy Optical Neural Networks on Thin-Film Lithium Niobate
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Title of host publication | CLEO: Science and Innovations 2024 |
Publisher | Optical Society of America |
ISBN (electronic) | 978-1-957171-39-5 |
Publication status | Published - May 2024 |
Publication series
Name | CLEO: Science and Innovations, CLEO: S and I in Proceedings CLEO, Part of Conference on Lasers and Electro-Optics |
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Conference
Title | Conference on Lasers and Electro-Optics 2024 (CLEO 2024) |
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Place | United States |
City | Charlotte |
Period | 5 - 10 May 2024 |
Link(s)
Abstract
We demonstrate a large-scale wavelength-time-space-multiplexed optical neural network using high-bandwidth (>40 GHz) electro-optic modulators at CMOS-compatible voltages (Vπ=1.3 V). Parallel computing with 7 wavelengths (over 1-THz) achieves 6-bit precision for accurate image classification. © 2024 The Author(s) © Optica Publishing Group 2024
Citation Format(s)
Hyper-multiplexed, Ultralow-Energy Optical Neural Networks on Thin-Film Lithium Niobate. / Ou, Shaoyuan; Sludds, Alexander; Hamerly, Ryan et al.
CLEO: Science and Innovations 2024. Optical Society of America, 2024. SF1O.6 (CLEO: Science and Innovations, CLEO: S and I in Proceedings CLEO, Part of Conference on Lasers and Electro-Optics).
CLEO: Science and Innovations 2024. Optical Society of America, 2024. SF1O.6 (CLEO: Science and Innovations, CLEO: S and I in Proceedings CLEO, Part of Conference on Lasers and Electro-Optics).
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review