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Optimizing optical neural network design for enhanced compatibility with analog computation.

Zongyu Lu, Jinming Tao, Xiaoyu Wang

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    Summary
    This summary is machine-generated.

    This study optimizes optical neural networks (ONNs) by leveraging analog computation properties. Larger bias power and concave activation functions enhance performance and robustness, while optical pruning reduces component count.

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    Area of Science:

    • Photonics
    • Artificial Intelligence
    • Optical Computing

    Background:

    • Traditional digital neural network emulation limits optical neural network (ONN) potential.
    • Understanding analog computation's unique properties is crucial for optimizing ONNs.
    • Mach-Zehnder interferometer (MZI) networks offer a valuable case study for exploring these properties.

    Purpose of the Study:

    • To investigate the impact of analog computation characteristics on ONN performance.
    • To identify optimal component properties for ONNs, specifically for MZI networks.
    • To enhance ONN classification accuracy, robustness, and efficiency.

    Main Methods:

    • Analyzing the influence of analog computation on bias power and activation functions in MZI networks.
    • Evaluating the effect of optical pruning on ONN performance and component count.
    • Testing proposed optimizations across various datasets and parameters (ξ values).

    Main Results:

    • Larger bias power and concave activation functions significantly improve ONN classification accuracy (up to 35%).
    • Optical pruning reduces MZI count by two-thirds without compromising performance.
    • Optimized ONNs demonstrate enhanced robustness against MZI losses and phase errors.

    Conclusions:

    • Analog computation properties, when properly utilized, offer significant advantages for ONN design.
    • Specific design principles, including bias power, activation functions, and optical pruning, enhance ONN performance and resilience.
    • The findings are applicable beyond MZI networks, guiding the development of various optical neural network architectures.