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Acta Aeronautica et Astronautica Sinica ›› 2024, Vol. 45 ›› Issue (20): 630439.doi: 10.7527/S1000-6893.2024.30439

• Aeronautics Computing and Simulation Technique • Previous Articles    

Intelligent processing of optical neural networks: Technological evolution and future prospects

Junshen LI1,2,3, Xiangyan MENG1,2,3, Nuannuan SHI1,2,3, Wei LI1,2,3, Ninghua ZHU1,2,3, Ming LI1,2,3()   

  1. 1.Key Laboratory of Optoelectronic Materials and Devices,Institute of Semiconductors,Chinese Academy of Sciences,Beijing 100083,China
    2.School of Materials Science and Optoelectronic Technology,University of Chinese Academy of Sciences,Beijing 100190,China
    3.School of Electronics,Electrical and Communication Engineering,University of Chinese Academy of Sciences,Beijing 100049,China
  • Received:2024-03-25 Revised:2024-04-19 Accepted:2024-05-23 Online:2024-06-06 Published:2024-05-29
  • Contact: Ming LI E-mail:ml@semi.ac.cn
  • Supported by:
    Chinese Academy of Sciences Youth Innovation Promotion Association(2022111);National Natural Science Foundation of China(62235011)

Abstract:

Convolutional Neural Network (CNN) has shown a wide range of applications in the fields of face recognition, image classification, machine vision, medical imaging, and aerospace due to its excellent feature extraction capabilities. However, traditional electrical intelligent processing chips are restricted by Moore’‍s law, and is difficult to meet the continuous growth of CNN computing power demand. With its characteristics of ultra-large broadband and ultra-low loss, light wave is a disruptive technology that supports the high computing power demand of the next generation of artificial intelligence. With optical or electrical high-dimensional control structure as the basic unit, it can realize computing through controlled propagation of light. In this paper, the research progress and technological breakthroughs of optical convolutional neural networks are reviewed. The overall trend of their development and the technical problems that need to be solved in the future are summarized. The prospects of optical convolutional neural networks for application are also discussed.

Key words: convolution, tensor, neural networks, artificial intelligence, deep learning

CLC Number: