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Model-free optimization and parallel architecture towards monolithic-hybrid-photonic-electronic reservoir computing.

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    This study introduces a new algorithm for photonic reservoir computing, enhancing machine learning performance. The Lyapunov filtered-minimal redundancy maximal relevance (Lf-mRMR) algorithm optimizes photonic systems for superior results.

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    Area of Science:

    • Neuromorphic Computing
    • Photonics
    • Machine Learning

    Background:

    • Physical reservoir computing (PRC) leverages nonlinear physical systems for machine learning.
    • Monolithic-hybrid-photonic-electronic reservoir computing (MHPE RC) combines photonic nonlinearity with electronic tunability.

    Purpose of the Study:

    • To analyze the performance of photonic waveguide meshes (WGMs) in MHPE RC.
    • To develop and validate an optimization algorithm for enhanced MHPE RC performance.

    Main Methods:

    • Numerical demonstration of parallel WGM architecture.
    • Development of the Lyapunov filtered-minimal redundancy maximal relevance (Lf-mRMR) algorithm for parameter optimization.
    • Experimental validation using on-chip silicon photonics.

    Main Results:

    • The parallel WGM architecture shows efficiency and performance superiority.
    • The Lf-mRMR algorithm improves MHPE RC performance, tolerates fabrication errors, and reduces computational complexity.
    • The selective parallel architecture for reservoir computing (SPARC) achieves performance comparable to convolutional neural networks.

    Conclusions:

    • The Lf-mRMR algorithm significantly enhances MHPE RC performance.
    • On-chip silicon photonics successfully validates the advantageous performance of Lf-mRMR-assisted RC.
    • This approach offers a computationally efficient and robust method for advanced machine learning tasks.