Deep neural networks in the cloud: Review, applications, challenges and research directions
Kit Yan Chana, Bilal Abu-Salihb, Raneem Qaddourac, Ala’ M. Al-Zoubid, Vasile Paladee, Duc-Son Phama, Javier Del Serf g, Khan Muhammadh✉
- aSchool of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, Perth, WA 6102, Australia
- bSchool for Information Technology, The University of Jordan, Amman, Jordan
- cSchool of Computing and informatics, Al Hussein Technical University, Jordan
- dSchool of Science, Technology and Engineering, University of Granada, Granada, Spain
- eCentre for Computational Science and Mathematical Modelling, Coventry University, Priory Street, Coventry, UK
- fTECNALIA (Basque Research & Technology Alliance – BRTA), P. Tecnologico, Ed. 700. 48170 Derio (Bizkaia), Spain
- gUniversity of the Basque Country (UPV/EHU), 48013 Bilbao (Bizkaia), Spain
- hVIS2KNOW Lab, Department of Applied Artificial Intelligence, School of Convergence, College of Computing and Informatics, Sungkyunkwan University, Seoul 03063, Republic of Korea
- ✉Corresponding author
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Abstract
Deep neural networks (DNNs) are currently being deployed as machine learning technology in a wide range of important real-world applications. DNNs consist of a huge number of parameters that require millions of floating-point operations (FLOPs) to be executed both in learning and prediction modes. A more effective method is to implement DNNs in a cloud computing system equipped with centralized servers and data storage sub-systems with high-speed and high-performance computing capabilities. This paper presents an up-to-date survey on current state-of-the-art deployed DNNs for cloud computing. Various DNN complexities associated with different architectures are presented and discussed alongside the necessities of using cloud computing. We also present an extensive overview of different cloud computing platforms for the deployment of DNNs and discuss them in detail. Moreover, DNN applications already deployed in cloud computing systems are reviewed to demonstrate the advantages of using cloud computing for DNNs. The paper emphasizes the challenges of deploying DNNs in cloud computing systems and provides guidance on enhancing current and new deployments.