Collection

Parallel Deep Learning for Heterogeneous Computing Architecture

Submission status
Closed
Heterogeneous Computing is one of the most important computing power cornerstones of Artificial Intelligence, which focuses on the big data description, GPU cloud service, data training, task scheduling, etc. Over the past few decades, the Heterogeneous Computing Architecture has achieved great progress in both the theories and applications. A typical pattern recognition system is composed of a set of heterogeneous machines, high-speed networks that connect heterogeneous machines, such as a commercialized network or a user specially designed, and corresponding heterogeneous computing support software. Nowadays, there are many opportunities and challenges to the field of Heterogeneous Computing. We should seek new Heterogeneous Computing theories to be adaptive to Artificial Intelligence. We should push forward new Heterogeneous Computing applications benefited from Artificial Intelligence. Deep learning plays an important role in machine learning field, and it has been widely used in the field of Heterogeneous Computing and machine learning. Especially, the Parallel Deep Learning has greatly affected the applications and developments of related fields, for instance, embedded cloudlet and multiple core systems in both academy and industry. It can be seen as a breakthrough to enhance or improve the Heterogeneous Computing Architecture. It is expected that the development and applications of parallel deep learning theories would further influence the field of Heterogeneous Computing. The special issue mainly focuses on parallel deep learning for Heterogeneous Computing Architecture. We are soliciting original contributions, of leading researchers and practitioners from academia as well as industry, which address a wide range of theoretical and application issues in deep learning for Heterogeneous Computing Architecture.

Editors

  • Kaijian Xia Kaijian Xia , 

    Kaijian Xia

    Kaijian Xia, received the Ph.D. degree at School of Information and Control Engineering from China University of Mining and Technology, Xuzhou, Jiangsu, China. His research interests include Parallel and Distributed Deep Learning, Intelligent Medical Information, Image Processing and Machine Learning. Dr. Xia has served as the TPC Vice-Chair for CyberLife 2019 and currently serves the Editor in Chief for International Journal of Health Systems and Translational Medicine, an Associate Editor for Journal of Medical Imaging and Health Informatics.

  • Tao Hu Tao Hu  &

    Tao Hu

    Tao Hu received the Ph.D. degree from Wuhan University, China. He was a post-doctoral research fellow at Kent State University. His research interests include spatiotemporal data analysis, big data computing, visualization and applications in public health. Dr. Hu has published over 20 papers, including Future Generation Computer Science, Multimedia Tools and Applications, International Journal of Public Health, Plos One and et al. He is also invited to be a reviewer for over 20 journals.

  • Wen Si Wen Si

    Wen Si

    Wen Si received his Ph.D. degree from Shanghai University. Now, he works in Clinical Setting and Rehabilitation Department, Huashan Hospital, Fudan University, as an assistant professor. He also works in University of south Florida as a visiting research assistant. He has published more than 20 scientific papers in Biomedical Engineering, Health Informatics and Internet of Things. His current research interests include wearable sensor, Human information collection, Medical Internet of things and Rehabilitation engineering.

Articles (27 in this collection)