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SCIENCE CHINA Information Sciences, Volume 64 , Issue 2 : 122401(2021) https://doi.org/10.1007/s11432-020-2998-1

Optoelectronic convolutional neural networks based on time-stretch method

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  • ReceivedMay 14, 2020
  • AcceptedJul 17, 2020
  • PublishedJan 20, 2021

Abstract


Acknowledgment

This work was supported by National Key Research and Development Program of China (Grant No. 2019YFB1803501), National Natural Science Foundation of China (Grant No. 61771284), and Beijing Natural Science Foundation (Grant No. L182043).


Supplement

Appendix A for lists of important variables and notations used in the article, Appendix B for the detailed generation of the the signals for modulation in each layer, Appendix C for the lists of feature maps extracted by electronical implemented CNN and TS-CNN under different relative noise levels, and Appendix D for the estimation of time consumption in TS-CNN.


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  • Figure 1

    (Color online) Structure of TS-CNN system. Light pulses propagate through the loop-shaped structure to implement linear computations in CNN. Index in circles represents important nodes whose signals will be shown in Subsection 3.2. Note that EDFA(s)1 represents one kind of untunable EDFA(s).

  • Figure 2

    (Color online) Structure of theoretical CNN model. It includes two convolution layers, two mean pooling layers and one full-connected layer. The sample `0' from the training set is used as an example to show all procedures.

  • Figure 3

    (Color online) Illustration of convolution operations implemented by broadened pulses in TS-CNN. All numbers and alphabets only represent pixel order.

  • Figure 4

    (Color online) Important signals of the last layer of TS-CNN system. (a) Pulses generated by mode-locked laser [\textcircled{1}]; (b) broadened pulses after pulse broaden component [\textcircled{2}]; (c) pulses after two intensity modulators [\textcircled{3}]; (d) zoomed-in picture of modulated pulse [\textcircled{3}]; (e) compressed pulses after compression component [\textcircled{4}]; (f) accumulated signals after PD [\textcircled{5}]. Note that index of circles in the square brackets represent the corresponding nodes in Figure 1. This picture depicts the convolution operations between the first kernel with input picture in convolution layer1 in TS-CNN system.

  • Figure 5

    (Color online) Confusion matrices of testing of TS-CNN. (a) The trend of accuracy of handwriting digit recognition with regard to the gain of adjustable EDFA2 in the optoelectronic TS-CNN system. Detailed information of confusion matrix of each data point can be seen in Figure 5(d)–(i). The numbers in circle mark the data points which are shown in Figure 5(a) with their corresponding confusion matrices. (b) Test of theoretical CNN by all pictures from MNIST test set. (c) Test of theoretical CNN by 100 pictures from MNIST test set. (d) Test of noise free TS-CNN [{\textcircled{1}}]. (e) Test of TS-CNN with the gain of EDFA2 equals 20 dB [{\textcircled{2}}]. (f) Test of TS-CNN with the gain of EDFA2 equals 17 dB [{\textcircled{3}}]. (g) Test of TS-CNN with the gain of EDFA2 equals 15 dB [{\textcircled{4}}]. (h) Test of TS-CNN with the gain of EDFA2 equals 12 dB [{\textcircled{5}}]. (i) Test of TS-CNN with the gain of EDFA2 equals 10 dB [{\textcircled{6}}].