Multiscale diffractive U-Net: a robust all-optical deep learning framework modeled with sampling and skip connections

Authors:
  • YLYiming Li
    Yiming Li
  • ZZZexi Zheng
    Zexi Zheng
  • RLRan Li
    Ran Li
  • QCQuan Chen
    Quan Chen
  • HLHaitao Luan
    Haitao Luan
  • HYHui Yang
    Hui Yang
  • QZQiming Zhang
    Qiming Zhang
  • MGMin Gu
    Min Gu
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Abstract
As an all-optical learning framework, diffractive deep neural networks (D2NNs) have great potential in running speed, data throughput, and energy consumption. The depth of networks and the misalignment of layers are two problems to limit its further development. In this work, a robust all-optical network framework (multiscale diffractive U-Net, MDUNet) based on multi-scale features fusion has been proposed. The depth expansion and alignment robustness of the network can be significantly improved by introducing sampling and skip connections. Compared with common all-optical learning frameworks, MDUNet achieves the highest accuracy of 98.81% and 89.11% on MNIST and Fashion-MNIST respectively. The testing accuracy of MNIST and Fashion-MNIST can be further improved to 99.06% and 89.86% respectively by using the ensemble learning method to construct the optoelectronic hybrid neural network.
Funding Information
  • Shanghai Municipal Science and Technology Major Project
  • Shanghai Frontiers Science Center Program (2021-2025 No. 20)
  • National Key Research and Development Program of China (2021YFB2802000)
  • National Natural Science Foundation of China (11972212, 12002213, 12072200, 61975123)

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