Democratizing AI: A Comparative Study in Deep Learning Efficiency and Future Trends in Computational Processing
The exponential growth in data has intensified the demand for computational power to train large-scale deep learning models. However, the rapid growth in model size and complexity raises concerns about equal and fair access to computational resources, particularly under increasing energy and infrastructure constraints. GPUs have emerged as essential for accelerating such workloads. This study benchmarks four deep learning models (Conv6, VGG16, ResNet18, CycleGAN) using TensorFlow and PyTorch on Intel Xeon CPUs and NVIDIA Tesla T4 GPUs. Our experiments demonstrate that, on average, GPU training achieves speedups ranging from 11x to 246x depending on model complexity, with lightweight model...
| Authors | Lisan Al Amin, Md Ismail Hossain, Rupak Kumar Das et al. |
|---|---|
| Primary category | cs.PF |
| Published | 21 March 2026 |
| Last updated at source | 2 April 2026 |
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