Training Of Neural Network On A Fashion MNIST Dataset Using Openmp For Image Recognition

Mahule Roy

Abstract: This paper explores the application of OpenMP for accelerating the training of neural networks in Fashion MNIST data recognition. OpenMP's parallelization capabilities will be harnessed to distribute computation across multiple threads, enhancing efficiency. Leveraging the MNIST dataset, we aim to assess the impact of OpenMP on recognition accuracy, training time, and resource utilization. The study will encompass variations in thread count, processor architectures, and dataset sizes. The anticipated outcomes include improved efficiency and reduced training times, providing valuable insights for optimizing OpenMP configurations in fashion recognition systems.