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Training large-scale optoelectronic neural networks with dual-neuron optical-artificial learning

Author

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  • Xiaoyun Yuan

    (Tsinghua University
    Beijing National Research Center for Information Science and Technology (BNRist)
    Tsinghua University (THUIBCS))

  • Yong Wang

    (Tsinghua University)

  • Zhihao Xu

    (Tsinghua University
    Tsinghua Shenzhen International Graduate School)

  • Tiankuang Zhou

    (Tsinghua University
    Beijing National Research Center for Information Science and Technology (BNRist)
    Tsinghua University (THUIBCS))

  • Lu Fang

    (Tsinghua University
    Beijing National Research Center for Information Science and Technology (BNRist)
    Tsinghua University (THUIBCS))

Abstract

Optoelectronic neural networks (ONN) are a promising avenue in AI computing due to their potential for parallelization, power efficiency, and speed. Diffractive neural networks, which process information by propagating encoded light through trained optical elements, have garnered interest. However, training large-scale diffractive networks faces challenges due to the computational and memory costs of optical diffraction modeling. Here, we present DANTE, a dual-neuron optical-artificial learning architecture. Optical neurons model the optical diffraction, while artificial neurons approximate the intensive optical-diffraction computations with lightweight functions. DANTE also improves convergence by employing iterative global artificial-learning steps and local optical-learning steps. In simulation experiments, DANTE successfully trains large-scale ONNs with 150 million neurons on ImageNet, previously unattainable, and accelerates training speeds significantly on the CIFAR-10 benchmark compared to single-neuron learning. In physical experiments, we develop a two-layer ONN system based on DANTE, which can effectively extract features to improve the classification of natural images.

Suggested Citation

  • Xiaoyun Yuan & Yong Wang & Zhihao Xu & Tiankuang Zhou & Lu Fang, 2023. "Training large-scale optoelectronic neural networks with dual-neuron optical-artificial learning," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-42984-y
    DOI: 10.1038/s41467-023-42984-y
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