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arXiv · 2312.16904

Block Pruning for Enhanced Efficiency in Convolutional Neural Networks

Cheng-En Wu·Azadeh Davoodi·Yu Hen Hu
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Abstract

This paper presents a novel approach to network pruning, targeting block pruning in deep neural networks for edge computing environments. Our method diverges from traditional techniques that utilize proxy metrics, instead employing a direct block removal strategy to assess the impact on classification accuracy. This hands-on approach allows for an accurate evaluation of each block's importance. We conducted extensive experiments on CIFAR-10, CIFAR-100, and ImageNet datasets using ResNet architectures. Our results demonstrate the efficacy of our method, particularly on large-scale datasets like ImageNet with ResNet50, where it excelled in reducing model size while retaining high accuracy, even when pruning a significant portion of the network. The findings underscore our method's capability in maintaining an optimal balance between model size and performance, especially in resource-constrained edge computing scenarios.

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Cheng-En Wu, Azadeh Davoodi, Yu Hen Hu. 2023-12-28. Block Pruning for Enhanced Efficiency in Convolutional Neural Networks. https://arxiv.org/abs/2312.16904

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