Welcome to the InfoSci Platform
Reference Hub5
Image Classification Using CNN With Multi-Core and Many-Core Architecture

Image Classification Using CNN With Multi-Core and Many-Core Architecture

Debajit Datta, Saira Banu Jamalmohammed
Copyright: © 2021 |Pages: 34
ISBN13: 9781799833352|ISBN10: 1799833356|ISBN13 Softcover: 9781799833369|EISBN13: 9781799833376
DOI: 10.4018/978-1-7998-3335-2.ch016
Cite Chapter Cite Chapter

MLA

Datta, Debajit, and Saira Banu Jamalmohammed. "Image Classification Using CNN With Multi-Core and Many-Core Architecture." Applications of Artificial Intelligence for Smart Technology, edited by P. Swarnalatha and S. Prabu, IGI Global, 2021, pp. 233-266. https://doi.org/10.4018/978-1-7998-3335-2.ch016

APA

Datta, D. & Jamalmohammed, S. B. (2021). Image Classification Using CNN With Multi-Core and Many-Core Architecture. In P. Swarnalatha & S. Prabu (Eds.), Applications of Artificial Intelligence for Smart Technology (pp. 233-266). IGI Global. https://doi.org/10.4018/978-1-7998-3335-2.ch016

Chicago

Datta, Debajit, and Saira Banu Jamalmohammed. "Image Classification Using CNN With Multi-Core and Many-Core Architecture." In Applications of Artificial Intelligence for Smart Technology, edited by P. Swarnalatha and S. Prabu, 233-266. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-3335-2.ch016

Export Reference

Mendeley
Favorite

Abstract

Image classification is a widely discussed topic in this era. It covers a vivid range of application domains like from garbage classification applications to advanced fields of medical sciences. There have been several research works that have been done in the past and are also currently under research for coming up with better-optimized image classification techniques. However, the process of image classification turns out to be time-consuming. This work deals with the widely accepted FashionMNIST (modified national institute of standards and technology database) dataset, having a set of sixty thousand images for training a model and another popular dataset of MNIST for handwritten numbers. The work compares several convolutional neural network (CNN) models and aims in parallelizing them using a distributed framework that is provided by the python library, RAY. The parallelization has been achieved over the multiple cores of CPU and many cores of GPU. The work also shows that the overall accuracy of the system is not affected by the parallelization.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.