Artificial Intelligence Platform for Mobile Service Computing

Since the birth of artificial intelligence, the theory and the technology have become more mature, and the application field is expanding. Mobile networks and applications have grown quickly in recent years, and mobile computing is the new computing paradigm for mobile networks. In this paper, we bu...

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Bibliographic Details
Published in:Journal of signal processing systems 2019-10, Vol.91 (10), p.1179-1189
Main Authors: Zhang, Haikuo, Lu, Zhonghua, Xu, Ke, Pang, Yuchen, Liu, Fang, Chen, Liandong, Wang, Jue, Wang, Yangang, Cao, Rongqiang
Format: Article
Language:English
Subjects:
Artificial intelligence
Circuits and Systems
Clusters
Computer Imaging
Electrical Engineering
Engineering
Image Processing and Computer Vision
Machine learning
Mobile communications networks
Mobile computing
Pattern Recognition
Pattern Recognition and Graphics
Schedules
Scheduling
Signal,Image and Speech Processing
Vision
Wireless networks
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Summary:Since the birth of artificial intelligence, the theory and the technology have become more mature, and the application field is expanding. Mobile networks and applications have grown quickly in recent years, and mobile computing is the new computing paradigm for mobile networks. In this paper, we build an artificial intelligence platform for a mobile service, which supports deep learning frameworks such as TensorFlow and Caffe. We describe the overall architecture of the AI platform for a GPU cluster in mobile service computing. In the GPU cluster, based on the scheduling layer, we propose Yarn by the Slurm scheduler to not only improve the distributed TensorFlow plug-in for the Slurm scheduling layer but also to extend YARN to manage and schedule GPUs. The front-end of the high-performance AI platform has the attributes of availability, scalability and efficiency. Finally, we verify the convenience, scalability, and effectiveness of the AI platform by comparing the performance of single-chip and distributed versions for the TensorFlow, Caffe and YARN systems.
ISSN:1939-8018
1939-8115
DOI:10.1007/s11265-019-1438-3