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
  • [41] Efficient densely connected convolutional neural networks
    Li, Guoqing
    Zhang, Meng
    Li, Jiaojie
    Lv, Feng
    Tong, Guodong
    [J]. PATTERN RECOGNITION, 2021, 109
  • [42] Classification of Transposable Elements by Convolutional Neural Networks
    da Cruz, Murilo H. P.
    Saito, Priscila T. M.
    Paschoal, Alexandre R.
    Bugatti, Pedro H.
    [J]. ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING, ICAISC 2019, PT II, 2019, 11509 : 157 - 168
  • [43] Convolutional and generative adversarial neural networks in manufacturing
    Kusiak, Andrew
    [J]. INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 2020, 58 (05) : 1594 - 1604
  • [44] Efficient Computation of Robustness of Convolutional Neural Networks
    Arcaini, Paolo
    Bombarda, Andrea
    Bonfanti, Silvia
    Gargantini, Angelo
    [J]. THIRD IEEE INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE TESTING (AITEST 2021), 2021, : 21 - 28
  • [45] Vector-kernel convolutional neural networks
    Ou, Jun
    Li, Yujian
    [J]. NEUROCOMPUTING, 2019, 330 : 253 - 258
  • [46] A Mixed Signal Architecture for Convolutional Neural Networks
    Lou, Qiuwen
    Pan, Chenyun
    McGuinness, John
    Horvath, Andras
    Naeemi, Azad
    Niemier, Michael
    Hu, X. Sharon
    [J]. ACM JOURNAL ON EMERGING TECHNOLOGIES IN COMPUTING SYSTEMS, 2019, 15 (02)
  • [47] Movie Genre Classification with Convolutional Neural Networks
    Simoes, Gabriel S.
    Wehrmann, Jonatas
    Barros, Rodrigo C.
    Ruiz, Duncan D.
    [J]. 2016 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2016, : 259 - 266
  • [48] Using Convolutional Neural Networks for Emoticon Classification
    Burnik, K.
    Knezevic, D. Bjelobrk
    [J]. 2019 42ND INTERNATIONAL CONVENTION ON INFORMATION AND COMMUNICATION TECHNOLOGY, ELECTRONICS AND MICROELECTRONICS (MIPRO), 2019, : 1614 - 1618
  • [49] ENVIRONMENTAL SOUND CLASSIFICATION WITH CONVOLUTIONAL NEURAL NETWORKS
    Piczak, Karol J.
    [J]. 2015 IEEE INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING, 2015,
  • [50] Hyperspectral Image Classification with Convolutional Neural Networks
    Slavkovikj, Viktor
    Verstockt, Steven
    De Neve, Wesley
    Van Hoecke, Sofie
    Van de Walle, Rik
    [J]. MM'15: PROCEEDINGS OF THE 2015 ACM MULTIMEDIA CONFERENCE, 2015, : 1159 - 1162