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Multiplexable all-optical nonlinear activator for optical computing.

Caihong Teng, Xuhao Zhang, Jindao Tang

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    |June 11, 2024
    PubMed
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    Researchers developed an all-optical nonlinear activator using stimulated Brillouin scattering (SBS) to overcome limitations in optical neural networks (ONNs). This SBS-based activator enables crucial nonlinear functions for faster, more energy-efficient optical computing.

    Area of Science:

    • Photonics and Optical Engineering
    • Artificial Intelligence Hardware
    • Nonlinear Optics

    Background:

    • Electronic neural networks face limitations in energy consumption and speed.
    • Optical neural networks (ONNs) promise enhanced performance but lack efficient optical nonlinearity.
    • Stimulated Brillouin scattering (SBS) offers unique carrier dynamics for optical applications.

    Purpose of the Study:

    • To propose and demonstrate an all-optical nonlinear activator for ONNs.
    • To leverage SBS for achieving essential nonlinear activation functions.
    • To address the bottleneck of optical nonlinearity in ONN development.

    Main Methods:

    • Experimental demonstration of an all-optical nonlinear activator.
    • Utilizing stimulated Brillouin scattering (SBS) for nonlinear optical activation.

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  • Implementation of saturable absorption and Rectified Linear Unit (ReLU) models.
  • Numerical simulations to verify performance as an ONN mapping unit.

    • Main Results:

    • The SBS-based activator supports both saturable absorption and ReLU nonlinear functions.
    • Achieved a large dynamic response bandwidth of approximately 11.24 GHz.
    • Demonstrated a low nonlinear threshold of approximately 2.29 mW.
    • Exhibited high stability and wavelength division multiplexing capabilities.

    Conclusions:

    • The proposed SBS activator effectively provides optical nonlinearity for ONNs.
    • The device shows potential for realizing high-performance, energy-efficient all-optical neural networks.
    • Simulation results confirm comparable performance to conventional activation functions, paving the way for practical ONNs.