Block Pruning for Enhanced Efficiency in Convolutional Neural Networks

• URL: http://arxiv.org/abs/2312.16904v2
• Date: Sun, 14 Jan 2024 13:31:40 GMT
• Title: Block Pruning for Enhanced Efficiency in Convolutional Neural Networks
• Authors: Cheng-En Wu, Azadeh Davoodi, Yu Hen Hu
• Abstract summary: 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.
• Score: 7.110116320545541
• License: http://creativecommons.org/publicdomain/zero/1.0/
• 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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