Strategies for training optical neural networks

被引：0

作者：

Qipeng Yang ^{[1
]}

Bowen Bai ^{[1
]}

Weiwei Hu ^{[1
]}

Xingjun Wang ^{[1
,2
,3
,4
]}

机构：

[1] State Key Laboratory of Advanced Optical Communications System and Networks, School of Electronics, Peking University

[2] Yangtze Delta Institute of Optoelectronics, Peking University

[3] Frontiers Science Center for Nano-optoelectronics, Peking University

[4] Peng Cheng Laboratory

来源：

National Science Open | 2022年 / 1卷 / 03期

基金：

中国博士后科学基金; 中国国家自然科学基金;

关键词：

D O I：

暂无

中图分类号：

TP183 [人工神经网络与计算];

学科分类号：

摘要：

Deep learning has flourished in different areas in recent years, such as computer vision and natural language processing. However, with the end of Moore’s law, these applications that rely heavily on computing power are facing bottlenecks. Optical neural networks(ONNs) [1] use light to perform calculations [2,3] featuring high speed and low energy consumption, and they are widely regarded as the next-generation application-specific integrated circuit(ASIC) [4,5] for artificial intelligence.

引用

页码：7 / 11

页数：5

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