Full text loading...
- ISSN: 2352-0965
- E-ISSN: 2352-0973
-
Comparative Analysis of CNN Performances Using CIFAR-100 and MNIST Databases: GPU vs. CPU Efficiency
- Source: Recent Advances in Electrical & Electronic Engineering, Volume 18, Issue 10, Dec 2025, p. 2025 - 2037
-
- 26 Aug 2024
- 24 Dec 2024
- 16 Jan 2025
Abstract
Introduction
Convolutional Neural Networks (CNNs) and deep learning algorithms have significantly advanced image processing and classification. This study compares three CNN architectures VGG-16, ResNet-50, and ResNet-18, and evaluates their performance on the CIFAR-100 and MNIST datasets. Training time is prioritized as a critical metric, along with test accuracy and training loss, under varying hardware configurations (NVIDIA-GPU and Intel-CPU).
Methods
Experiments were conducted on an Ubuntu 22.04 system using the PyTorch framework. The hardware configurations included an NVIDIA GeForce GTX 1660 Super GPU and an Intel Core i5-10400 CPU. The CIFAR-100 dataset, containing 60,000 color images across 100 classes, and the MNIST dataset, comprising 70,000 grayscale images, were used for benchmarking.
Results
The results highlight the superior efficiency of GPUs, with training times reduced by up to 10x compared to CPUs. For CIFAR-100, VGG-16 required 13,000 seconds on the GPU versus 130,000 seconds on the CPU, while ResNet-18, the most time-efficient model, completed training in 150 seconds on the GPU and 1,740 seconds on the CPU. ResNet-50 achieved the highest test accuracy (~80%) on CIFAR-100. On MNIST, ResNet-18 was the most efficient, with training times of 185 seconds on the GPU and 22,000 seconds on the CPU.
Discussion
This study highlights the clear advantage of GPUs over CPUs in reducing training times, particularly for complex models such as VGG-16 and ResNet-50. ResNet-50 achieved the highest accuracy, while ResNet-18 was the most time efficient. However, the use of simpler datasets (MNIST and CIFAR-100) may not fully capture the complexity of the real world.
Conclusion
This study emphasizes the importance of hardware selection for deep learning workflows. Using the CIFAR-100 and MNIST datasets, we demonstrated that GPUs significantly outperform CPUs, achieving up to a 10x reduction in training time while maintaining competitive accuracy. Among the architectures tested, ResNet-50 delivered the highest test accuracy (~80%) on CIFAR-100, demonstrating superior feature extraction capabilities compared to VGG-16 and ResNet-18. Meanwhile, ResNet-18 proved to be the most time-efficient architecture, completing training in 150 seconds on the GPU, a significant improvement over VGG-16's 13,000 seconds. These results highlight the advantage of residual connections in reducing training complexity and achieving higher performance. The results underscore the critical role of both architecture selection and hardware optimization in advancing deep learning workflows.
©
2025 Bentham Science Publishers