Parallelizing Convolution Neural Network for Image Classification

The problem in classifying images using CNN is that it is a time consuming process. Actually it takes minutes to identify one image in average cases. This case has been studied regarding its uses, types, and importance. Due to its several practices, it was very crucial to find some ways that decrease its needed time and maintaining the quality and efficiency of the program. CNN, in all its types used matrix calculations a lot to classify images which was one of the main reasons for the high needed time. Solving the problem begins by modifying these calculations and make them faster. Using several parallelism techniques such as MPI, OpenMP, Cuda C can be of great assistance in this case. Although those methods are different in terms of applications, all of them will be used to increase the speed of the CNN while maintaining its accuracy. Experimental results show that these techniques were different in terms of needed time but similar in being very less than the sequential version of the CNN. It is recommended that future studies should research other types of image classification and parallel techniques.