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Structured Pruning for Deep Convolutional Neural Networks: A Survey.

Yang He, Lingao Xiao

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    Summary
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    Structured pruning of deep Convolutional neural networks (CNNs) offers efficient model compression. This survey details state-of-the-art structured pruning techniques, addressing challenges and future research directions for practical acceleration.

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    Area of Science:

    • Artificial Intelligence
    • Computer Science
    • Machine Learning

    Background:

    • Deep Convolutional Neural Networks (CNNs) achieve high performance through complex architectures, leading to substantial computational costs.
    • Neural network pruning is crucial for reducing storage and computational demands.
    • Structured pruning offers hardware-friendly acceleration, unlike unstructured weight pruning.

    Purpose of the Study:

    • To survey recent advancements in structured pruning for deep CNNs.
    • To compare state-of-the-art structured pruning techniques.
    • To identify challenges and future research opportunities in structured pruning.

    Main Methods:

    • Comparison of structured pruning techniques based on filter ranking, regularization, dynamic execution, and neural architecture search.
    • Discussion of the lottery ticket hypothesis and pruning applications.
    • Brief introduction to unstructured pruning for contrast.

    Main Results:

    • A comprehensive overview of current structured pruning methodologies.
    • Comparative analysis of different structured pruning approaches.
    • Identification of key challenges and innovative solutions in the field.

    Conclusions:

    • Structured pruning is vital for efficient and practical deep CNN acceleration.
    • The field presents numerous opportunities for further research and development.
    • Resources for further exploration include curated paper lists and interactive comparison websites.