Abstract: The Convolutional Neural Network (CNN) models are effective in computer vision strategies and have gained popularity due to their strong performance in visual tasks. Nevertheless, models with architectures such as VGG19 are expensive in terms of computational resources and require huge memory, which limits their usage on low-end devices. The study examines how efficiency can be increased in the model VGG19 by using model compression techniques, like pruning (structured and un-structured) and quantization (8-bit and 4-bit Quantization-Aware Training - QAT). The efficiency of the individual compression approaches was tested by thoroughly exploring the VGG19 with the MNIST, CIFAR-10, and Oxford-IIIT Pet datasets. Each model was evaluated against the baseline based on measures of accuracy, model size, inference time, and complexities of the model, CPU usage, and memory usage. The applied QAT approach reduced the model size by 75% with a drop in computational cost across all methods. In addition, the 8-bit quantitative assessment allowed for substantial system compression alongside increased speed delivery with minimal impact on accuracy. The highest compression and sparsity achieved by 4-bit QAT was 48%, which was not effective as it reduced accuracy on complex datasets, with additional computational overhead on T4 GPU. Structured pruning resulted in faster inference, but unstructured pruning also demonstrated a good result in retaining accuracy and even improving it. To simplify the VGG19 structure, pruning and quantization mechanisms are suggested in order to simplify the architecture to implement the model on edge devices sufficiently, without compromising prediction performance.

Md. Mijanur Rahman, Anik Datta, Md. Sabiruzzaman and Md Samim Ahmed Bin Hossain, “Reducing Computational Complexity in CNNs: A Focus on VGG19 Pruning and Quantization” International Journal of Advanced Computer Science and Applications(IJACSA), 16(6), 2025. http://dx.doi.org/10.14569/IJACSA.2025.01606105

@article{Rahman2025,
title = {Reducing Computational Complexity in CNNs: A Focus on VGG19 Pruning and Quantization},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.01606105},
url = {http://dx.doi.org/10.14569/IJACSA.2025.01606105},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {6},
author = {Md. Mijanur Rahman and Anik Datta and Md. Sabiruzzaman and Md Samim Ahmed Bin Hossain}
}