Comparison of Performance of Parallel Computation of CPU Cores on CNN model

Algorithms such as Convolutional Neural Network used for image classification. When huge convolutional neural networks are used to classify image datasets it takes a lot of time to perform convolution operations, which results in increasing the computational demand of image processing. The better way to accelerate image processing is to parallelizing multiple CPU cores. CPU consists of a few numbers of cores that are optimized for sequential processing. 1GHz. This project aims to compare the performance of parallelized CPUs. To parallelize multicore CPUs Python’s Ray library is being used. Convolutional Neural Network is used as the benchmark image classification algorithm in this project. We have tried to show the speedup in training a model with parallelization of the multicores of a CPU. The comparative study in our work has been carried out amongst three different Convolutional Neural Network models namely, the AlexNet model, the VGG16 model and the model proposed by ‘emmarex’ which we found on the online deep-learning platform, called Kaggle. The dataset used in this project is Plant Disease Image Dataset.