120 GOPS Photonic Tensor Core in Thin-film Lithium Niobate for Inference and in-situ Training
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arXiv
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| Natura: | Preprint |
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2023
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| 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 |
| author_facet | 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 |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_16896 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | 120 GOPS Photonic Tensor Core in Thin-film Lithium Niobate for Inference and in-situ Training 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 Optics Emerging Technologies Applied Physics 78A05 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. |
| title | 120 GOPS Photonic Tensor Core in Thin-film Lithium Niobate for Inference and in-situ Training |
| topic | Optics Emerging Technologies Applied Physics 78A05 |
| url | https://arxiv.org/abs/2311.16896 |