Hyper-multiplexed, Ultralow-Energy Optical Neural Networks on Thin-Film Lithium Niobate

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

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Author(s)

  • Shaoyuan Ou
  • Alexander Sludds
  • Ryan Hamerly
  • Eric Zhong
  • Dirk Englund
  • Mengjie Yu
  • Zaijun Chen

Detail(s)

Original languageEnglish
Title of host publicationCLEO: Science and Innovations 2024
PublisherOptical Society of America
ISBN (electronic)978-1-957171-39-5
Publication statusPublished - May 2024

Publication series

NameCLEO: Science and Innovations, CLEO: S and I in Proceedings CLEO, Part of Conference on Lasers and Electro-Optics

Conference

TitleConference on Lasers and Electro-Optics 2024 (CLEO 2024)
PlaceUnited States
CityCharlotte
Period5 - 10 May 2024

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).

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review