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Photonics-enabled spiking timing-dependent convolutional neural network for real-time image classification.

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    |October 12, 2022
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    A novel photonics-enabled spiking timing-dependent convolutional neural network (CNN) accelerates machine learning. This innovative approach uses multidimensional photonics parameters for real-time image recognition with high accuracy.

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    Area of Science:

    • Optoelectronics
    • Artificial Intelligence
    • Neuromorphic Computing

    Background:

    • Traditional convolutional neural networks (CNNs) have limitations in processing speed and energy efficiency for complex tasks.
    • Accelerating machine learning and neuromorphic computing is crucial for advancing information processing.

    Purpose of the Study:

    • To propose and demonstrate a photonics-enabled spiking timing-dependent CNN architecture.
    • To leverage multidimensional photonics parameters for enhanced computational capabilities.
    • To achieve real-time image recognition with improved performance.

    Main Methods:

    • A photonics convolution processor was designed using multidimensional multiplexing.
    • Key components included an active mode-locked laser, dispersion compensation fiber, and waveshaper.
    • The processor was integrated with an electronic full connection for classification.

    Main Results:

    • The photonics-enabled CNN achieved a 90.04% prediction accuracy on the MNIST handwritten digit database.
    • The architecture demonstrated real-time image recognition capabilities.
    • The system effectively mapped CNN operations into a time-dependent series structure.

    Conclusions:

    • Photonics offers a viable pathway to accelerate conventional neural networks and neuromorphic computing.
    • The proposed architecture shows potential for widespread application in information processing tasks.
    • Future applications include goods classification, vowel recognition, and speech identification.