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
收藏
页码：7 / 11
页数：5
相关论文
共 50 条
  • [21] NearUni: Near-Unitary Training for Efficient Optical Neural Networks
    Eldebiky, Amro
    Li, Bing
    Zhang, Grace Li
    2023 IEEE/ACM INTERNATIONAL CONFERENCE ON COMPUTER AIDED DESIGN, ICCAD, 2023,
  • [22] From neural networks to neural strategies
    Goerick, C
    Sendhoff, B
    vonSeelen, W
    1997 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS I - V: VOL I: PLENARY, EXPERT SUMMARIES, SPECIAL, AUDIO, UNDERWATER ACOUSTICS, VLSI; VOL II: SPEECH PROCESSING; VOL III: SPEECH PROCESSING, DIGITAL SIGNAL PROCESSING; VOL IV: MULTIDIMENSIONAL SIGNAL PROCESSING, NEURAL NETWORKS - VOL V: STATISTICAL SIGNAL AND ARRAY PROCESSING, APPLICATIONS, 1997, : 119 - 122
  • [23] Training strategies for critic and action neural networks in dual heuristic programming method
    Lendaris, GG
    Paintz, C
    1997 IEEE INTERNATIONAL CONFERENCE ON NEURAL NETWORKS, VOLS 1-4, 1997, : 712 - 717
  • [24] Lightweight and Elegant Data Reduction Strategies for Training Acceleration of Convolutional Neural Networks
    Demidovskij, Alexander
    Tugaryov, Artyom
    Trutnev, Aleksei
    Kazyulina, Marina
    Salnikov, Igor
    Pavlov, Stanislav
    MATHEMATICS, 2023, 11 (14)
  • [25] Comparison of Training Strategies for the Segmentation of Retina Layers in Optical Coherence Tomography Images of Rodent Eyes using Convolutional Neural Networks
    Chen, Antong
    Ong, Charlene Zhi Lin
    Luo, Weiwei
    Lee, Chen Fei
    Chia, Ser Mien
    Galvao, Joana
    Metzger, Daniel
    Gifford, Eric
    Chin, Chih-Liang
    Ali, Asad Abu Bakar
    MEDICAL IMAGING 2020: IMAGE PROCESSING, 2021, 11313
  • [26] OPTICAL NEURAL NETWORKS
    CAULFIELD, HJ
    KINSER, J
    ROGERS, SK
    PROCEEDINGS OF THE IEEE, 1989, 77 (10) : 1573 - 1583
  • [27] Optical neural networks
    Cartlidge E.
    Optics and Photonics News, 2020, 31 (06): : 32 - 39
  • [28] Backpropagation through nonlinear units for the all-optical training of neural networks
    Guo, Xianxin
    Barrett, Thomas D.
    Wang, Zhiming M.
    Lvovsky, A., I
    PHOTONICS RESEARCH, 2021, 9 (03) : B71 - B80
  • [29] Study of Training Patterns for Employing Deep Neural Networks in Optical Communication Systems
    Chuang, Chun-Yen
    Liu, Li-Chun
    Wei, Chia-Chien
    Liu, Jun-Jie
    Henrickson, Lindor
    Wang, Chih-Lin
    Chen, Young-Kai
    Chen, Jyehong
    2018 EUROPEAN CONFERENCE ON OPTICAL COMMUNICATION (ECOC), 2018,
  • [30] Backpropagation through nonlinear units for the all-optical training of neural networks
    XIANXIN GUO
    THOMAS DBARRETT
    ZHIMING MWANG
    AILVOVSKY
    Photonics Research, 2021, (03) : 262 - 271
12345