A structured pruning method based on MobileNetV3

Haixia Pan; Shiheng Wang; Huolong Ye; Sinan Lin

doi:10.1117/12.3052027

18 November 2024 A structured pruning method based on MobileNetV3

Haixia Pan, Shiheng Wang, Huolong Ye, Sinan Lin

Author Affiliations +

Proceedings Volume 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) ; 134030Z (2024) https://doi.org/10.1117/12.3052027
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, 2024, Zhengzhou, China

Abstract

In recent years, deep neural networks have made significant advancements, surpassing traditional models in computer vision. However, the vast number of parameters in deep models poses challenges such as increased network size, sluggish computation, high computational costs, and extensive storage requirements, thus hindering deployment on resource-constrained platforms like mobile devices. This paper focuses on compressing convolutional neural networks to address these challenges. This paper proposes a structured pruning method that integrates MobileNet features to prune the MobileNetV3-Large model, resulting in a more compact and efficient model. The approach involves sparse regularization training to obtain a sparser network model, which is followed up by structured pruning. This pruning is performed by leveraging the product of the sparsity values of convolutional layers and the scaling factors of batch normalization layers to identify and remove redundant filters. Experiments were conducted on the CIFAR-10 dataset. The experimental results demonstrate that the proposed compression method effectively reduces model parameters, decreases model size, minimizes runtime memory usage, and diminishes computational operations, while maintaining comparable performance levels despite the compression. Specifically, for MobileNetV3-Large, the model's parameter count is reduced by 40%, and computational operations are reduced by 35%, all while maintaining the same level of accuracy.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Haixia Pan, Shiheng Wang, Huolong Ye, and Sinan Lin "A structured pruning method based on MobileNetV3", Proc. SPIE 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) , 134030Z (18 November 2024); https://doi.org/10.1117/12.3052027

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KEYWORDS

Tunable filters

Convolution

Neural networks

Education and training

Visual process modeling

Performance modeling

Batch normalization

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