光子学
计算机科学
人工神经网络
光子集成电路
领域(数学)
电子工程
人工智能
工程类
材料科学
光电子学
数学
纯数学
作者
Kun Liao,Tianxiang Dai,Qiuchen Yan,Xiaoyong Hu,Qihuang Gong
出处
期刊:ACS Photonics
[American Chemical Society]
日期:2023-02-07
卷期号:10 (7): 2001-2010
被引量:54
标识
DOI:10.1021/acsphotonics.2c01516
摘要
Photonic neural networks benefit from the use of photons to perform intelligent inference computing with ultrafast and ultralow energy consumption in ultra-high-throughput, providing the efficient photonic hardware for the new generation of intelligent computing, and the effective way to support large-scale integration for on-chip all-optical computing chips. With the rapid development of photonic neural networks, demands for efficient computation power have increased dramatically. However, the weak and impractical optical nonlinear activations, the lack of suitable configurations for integrated photonic hardware, and proper optical storage mediums pose challenges to this field. In this Perspective, we propose our current point of view and a suggestive roadmap in the field of integrated photonic platform for optical neural networks. Throughout the discussion, we highlight recent progresses meeting with major challenges. We also identify some next challenges still ahead to realize integrated photonic neural networks capable of matching the current computational power of graphic cards.
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