Open AccessImage Classification using Parallel CPU and GPU Computing
Author(s) -
Prathamesh Borhade,
Rajvardhan Deshmukh,
Samridhi Murarka,
Rishi Agarwal
Publication year - 2020
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.d7870.049420
Subject(s) - computer science , speedup , convolutional neural network , central processing unit , parallel computing , graphics processing unit , multi core processor , cuda , image processing , benchmark (surveying) , general purpose computing on graphics processing units , graphics , artificial intelligence , image (mathematics) , computer hardware , computer graphics (images) , geodesy , geography
Image classification algorithms such as Convolutional Neural Network used for classifying huge image datasets takes a lot of time to perform convolution operations, thus increasing the computational demand of image processing. Compared to CPU, Graphics Processing Unit (GPU) is a good way to accelerate the processing of the images. Parallelizing multiple CPU cores is also another way to process the images faster. Increasing the system memory (RAM) can also decrease the computational time of image processing. Comparing the architecture of CPU and GPU, the former consists of a few cores optimized for sequential processing whereas the later has thousands of relatively simple cores clocked at approx. 1Ghz. The aim of this project is to compare the performance of parallelized CPUs and a GPU. Python’s Ray library is being used to parallelize multicore CPUs. The benchmark image classification algorithm used in this project is Convolutional Neural Network. The dataset used in this project is Plant Disease Image Dataset. Our results show that the GPU implementation achieves 80% speedup compared to the CPU implementation.