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    Area of Science:

    • Neuromorphic Computing
    • Photonics
    • Optical Signal Processing

    Background:

    • High-speed photonic reservoir computing (RC) is crucial for advanced neuromorphic computing.
    • Current RC architectures face speed and capability limitations due to feedback loops and digital processing.

    Purpose of the Study:

    • To propose and demonstrate a novel non-feedback, fully-analog feed-forward photonic RC (FF-PhRC) system.
    • To overcome the limitations of existing RC architectures for high-speed applications.

    Main Methods:

    • Utilized optical dispersion-induced pulse broadening to implement the reservoir layer (RL).
    • Combined modulator multiplication and temporal integration for optoelectronic analog readout.
    • Employed wavelength-division multiplexing for parallel task processing.

    Main Results:

    • Successfully implemented linear regression for the analog readout layer.
    • Demonstrated FF-PhRC effectiveness in chaotic signal prediction, spoken digit recognition, and MNIST classification.
    • Achieved parallel processing capability up to 10 GHz per wavelength.

    Conclusions:

    • The proposed FF-PhRC system offers a high-performance, high-speed solution for real-time neuromorphic computing.
    • This non-feedback approach enhances the speed and capabilities of photonic RC systems.