120 GOPS Photonic tensor core in thin-film lithium niobate for inference and in situ training

120 GOPS Photonic tensor core in thin-film lithium niobate for inference and in situ training

Abstract Photonics offers a transformative approach to artificial intelligence (AI) and neuromorphic computing by enabling low-latency, high-speed, and energy-efficient computations. However, conventional photonic tensor cores face significant challenges in constructing large-scale photonic neuromor...

Full description

Saved in:
Bibliographic Details
Main Authors: Zhongjin Lin, Bhavin J. Shastri, Shangxuan Yu, Jingxiang Song, Yuntao Zhu, Arman Safarnejadian, Wangning Cai, Yanmei Lin, Wei Ke, Mustafa Hammood, Tianye Wang, Mengyue Xu, Zibo Zheng, Mohammed Al-Qadasi, Omid Esmaeeli, Mohamed Rahim, Grzegorz Pakulski, Jens Schmid, Pedro Barrios, Weihong Jiang, Hugh Morison, Matthew Mitchell, Xun Guan, Nicolas A. F. Jaeger, Leslie A. Rusch, Sudip Shekhar, Wei Shi, Siyuan Yu, Xinlun Cai, Lukas Chrostowski
Format: Article
Language:English
Published: Nature Portfolio 2024-10-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-024-53261-x
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://doi.org/10.1038/s41467-024-53261-x

Similar Items