Optical Convolutional Neural Networks: Methodology and Advances (Invited)

被引:3
|
作者
Meng, Xiangyan [1 ,2 ,3 ]
Shi, Nuannuan [1 ,2 ,3 ]
Li, Guangyi [1 ,2 ,3 ]
Li, Wei [1 ,2 ,3 ]
Zhu, Ninghua [1 ,2 ,3 ]
Li, Ming [1 ,2 ,3 ]
机构
[1] Inst Semicond, Chinese Acad Sci, State Key Lab Integrated Optoelect, Beijing 100083, Peoples R China
[2] Univ Chinese Acad Sci, Ctr Mat Sci & Optoelect Engn, Beijing 100190, Peoples R China
[3] Univ Chinese Acad Sci, Sch Elect Elect & Commun Engn, Beijing 100049, Peoples R China
来源
APPLIED SCIENCES-BASEL | 2023年 / 13卷 / 13期
基金
中国国家自然科学基金;
关键词
convolutional neural networks; optical computing; photonics signal processing; ARTIFICIAL-INTELLIGENCE; MOORES LAW; BACKPROPAGATION; DESIGN; CLASSIFICATION; ACCELERATOR;
D O I
10.3390/app13137523
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
As a leading branch of deep learning, the convolutional neural network (CNN) is inspired by the natural visual perceptron mechanism of living things, showing great application in image recognition, language processing, and other fields. Photonics technology provides a new route for intelligent signal processing with the dramatic potential of its ultralarge bandwidth and ultralow power consumption, which automatically completes the computing process after the signal propagates through the processor with an analog computing architecture. In this paper, we focus on the key enabling technology of optical CNN, including reviewing the recent advances in the research hotspots, overviewing the current challenges and limitations that need to be further overcome, and discussing its potential application.
引用
收藏
页数:19
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