Report
120 GOPS Photonic Tensor Core in Thin-film Lithium Niobate for Inference and in-situ Training
| العنوان: | 120 GOPS Photonic Tensor Core in Thin-film Lithium Niobate for Inference and in-situ Training |
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| المؤلفون: | 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 |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science Physics (Other) |
| مصطلحات موضوعية: | Physics - Optics, Computer Science - Emerging Technologies, Physics - Applied Physics, 78A05 |
| الوصف: | 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 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2311.16896 |
| رقم الانضمام: | edsarx.2311.16896 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |