  The process of training a deep neural network is characterized by significant
time requirements and associated costs. Although researchers have made
considerable progress in this area, further work is still required due to
resource constraints. This study examines innovative approaches to expedite the
training process of deep neural networks (DNN), with specific emphasis on three
state-of-the-art models such as ResNet50, Vision Transformer (ViT), and
EfficientNet. The research utilizes sophisticated methodologies, including
Gradient Accumulation (GA), Automatic Mixed Precision (AMP), and Pin Memory
(PM), in order to optimize performance and accelerate the training procedure.
  The study examines the effects of these methodologies on the DNN models
discussed earlier, assessing their efficacy with regard to training rate and
computational efficacy. The study showcases the efficacy of including GA as a
strategic approach, resulting in a noteworthy decrease in the duration required
for training. This enables the models to converge at a faster pace. The
utilization of AMP enhances the speed of computations by taking advantage of
the advantages offered by lower precision arithmetic while maintaining the
correctness of the model.
  Furthermore, this study investigates the application of Pin Memory as a
strategy to enhance the efficiency of data transmission between the central
processing unit and the graphics processing unit, thereby offering a promising
opportunity for enhancing overall performance. The experimental findings
demonstrate that the combination of these sophisticated methodologies
significantly accelerates the training of DNNs, offering vital insights for
experts seeking to improve the effectiveness of deep learning processes.
