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    This study introduces an evolutionary algorithm to create more efficient deep neural networks (DNNs) by reducing layers without losing performance. The method prunes unnecessary blocks and uses knowledge distillation for better results.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Deep neural networks (DNNs) offer high performance but face computational cost challenges.
    • Manually designed architectures may lack generalization across different domains.

    Purpose of the Study:

    • To develop a method for shallowing deep neural networks (DNNs) at block levels.
    • To reduce computational cost and improve generalization of DNNs.

    Main Methods:

    • An evolutionary algorithm (ESNB) was used for block-level DNN shallowing.
    • Multiobjective optimization pruned less informative blocks.
    • Knowledge distillation recovered performance, with a novel prior knowledge incorporation and correctness-aware strategy.

    Main Results:

    • ESNB effectively accelerated DNN inference.
    • The method achieved superior performance compared to state-of-the-art approaches.
    • Reduced network depth while maintaining or improving accuracy.

    Conclusions:

    • ESNB offers an effective approach to create efficient and generalizable DNNs.
    • The evolutionary strategy successfully balances network reduction and performance.
    • This method addresses the deployment challenges of DNNs on resource-constrained platforms.