학술논문
120 GOPS Photonic Tensor Core in Thin-film Lithium Niobate for Inference and in-situ Training
Document Type
Working Paper
Author
Lin, Zhongjin; Shastri, Bhavin J.; Yu, Shangxuan; Song, Jingxiang; Zhu, Yuntao; Safarnejadian, Arman; Cai, Wangning; Lin, Yanmei; Ke, Wei; Hammood, Mustafa; Wang, Tianye; Xu, Mengyue; Zheng, Zibo; Al-Qadasi, Mohammed; Esmaeeli, Omid; Rahim, Mohamed; Pakulski, Grzegorz; Schmid, Jens; Barrios, Pedro; Jiang, Weihong; Morison, Hugh; Mitchell, Matthew; Guan, Xun; Jaeger, Nicolas A. F.; Rusch, Leslie A. n; Shekhar, Sudip; Shi, Wei; Yu, Siyuan; Cai, Xinlun; Chrostowski, Lukas
Source
Subject
Language
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 neuromorphic networks. Here, we propose a fully integrated photonic tensor core, consisting of only two thin-film lithium niobate (TFLN) modulators, a III-V laser, and a charge-integration photoreceiver. Despite its simple architecture, it is capable of implementing an entire layer of a neural network with a computational speed of 120 GOPS, while also allowing flexible adjustment of the number of inputs (fan-in) and outputs (fan-out). Our tensor core supports rapid in-situ training with a weight update speed of 60 GHz. Furthermore, it successfully classifies (supervised learning) and clusters (unsupervised learning) 112 * 112-pixel images through in-situ training. To enable in-situ training for clustering AI tasks, we offer a solution for performing multiplications between two negative numbers.
Comment: 21 pages, 6 figures
Comment: 21 pages, 6 figures