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
相关论文
共 50 条
  • [31] Optical diagnosis of colorectal polyps using convolutional neural networks
    Kader, Rawen
    Hadjinicolaou, Andreas, V
    Georgiades, Fanourios
    Stoyanov, Danail
    Lovat, Laurence B.
    WORLD JOURNAL OF GASTROENTEROLOGY, 2021, 27 (35) : 5908 - 5918
  • [32] Optical Diffractive Convolutional Neural Networks Implemented in an All-Optical Way
    Yu, Yaze
    Cao, Yang
    Wang, Gong
    Pang, Yajun
    Lang, Liying
    SENSORS, 2023, 23 (12)
  • [33] Convolutional neural networks
    Alexander Derry
    Martin Krzywinski
    Naomi Altman
    Nature Methods, 2023, 20 : 1269 - 1270
  • [34] Convolutional neural networks
    Derry, Alexander
    Krzywinski, Martin
    Altman, Naomi
    NATURE METHODS, 2023, 20 (09) : 1269 - 1270
  • [35] A Data Augmentation Methodology to Improve Age Estimation using Convolutional Neural Networks
    Oliveira, Italo de Pontes
    Peixoto Medeiros, Joao Lucas
    de Sousa, Vinicius Fernandes
    Teixeira Junior, Adalberto Gomes
    Pereira, Eanes Torres
    Gomes, Herman Martins
    2016 29TH SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), 2016, : 88 - 95
  • [36] Auto-compression transfer learning methodology for deep convolutional neural networks
    Camacho, J. D.
    Villasenor, Carlos
    Gomez-Avila, Javier
    Lopez-Franco, Carlos
    Arana-Daniel, Nancy
    NEUROCOMPUTING, 2025, 630
  • [37] Class-specific early exit design methodology for convolutional neural networks
    Bonato, Vanderlei
    Bouganis, Christos-Savvas
    APPLIED SOFT COMPUTING, 2021, 107
  • [38] Recent advances in plant disease severity assessment using convolutional neural networks
    Shi, Tingting
    Liu, Yongmin
    Zheng, Xinying
    Hu, Kui
    Huang, Hao
    Liu, Hanlin
    Huang, Hongxu
    SCIENTIFIC REPORTS, 2023, 13 (01)
  • [39] Recent advances in plant disease severity assessment using convolutional neural networks
    Tingting Shi
    Yongmin Liu
    Xinying Zheng
    Kui Hu
    Hao Huang
    Hanlin Liu
    Hongxu Huang
    Scientific Reports, 13
  • [40] A Survey on the Applications of Convolutional Neural Networks for Synthetic Aperture Radar: Recent Advances
    Oveis, Amir Hosein
    Giusti, Elisa
    Ghio, Selenia
    Martorella, Marco
    IEEE AEROSPACE AND ELECTRONIC SYSTEMS MAGAZINE, 2022, 37 (05) : 18 - +