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Ross Drummond, Matthew C Turner, Stephen R Duncan

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    This study introduces a new method for creating smaller neural networks for on-device machine learning. It synthesizes reduced-order networks that minimize worst-case error, enhancing robustness for privacy-focused applications.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Increasing data generation on smartphones and cyber-physical systems necessitates on-device machine learning.
    • Existing on-device algorithms face hardware limitations, impacting privacy, latency, and energy efficiency.
    • Limited understanding of device-oriented algorithm behavior and training hinders optimization.

    Purpose of the Study:

    • To develop a method for automatically synthesizing reduced-order neural networks (NNs).
    • To approximate the input-output mapping of larger NNs for efficient on-device deployment.
    • To enhance the robustness of NNs against out-of-sample data points.

    Main Methods:

    • Introduced a novel approach to automatically synthesize reduced-order neural networks.
    • Utilized a convex semidefinite program to generate network weights and biases.
    • Minimized the worst-case approximation error between the reduced and larger networks.

    Main Results:

    • Successfully synthesized reduced-order neural networks approximating larger ones.
    • Obtained worst-case bounds for the approximation error.
    • Demonstrated the approach's applicability across various neural network architectures.

    Conclusions:

    • The proposed method offers a robust way to create smaller, efficient neural networks for edge devices.
    • Incorporating worst-case error minimization in training enhances robustness to new data.
    • This work generalizes neural network robustness analysis to a robust synthesis problem.