Energy-Aware Inference Offloading for DNN-Driven Applications in Mobile Edge Clouds

With increasing focus on Artificial Intelligence (AI) applications, Deep Neural Networks (DNNs) have been successfully used in a number of application areas. As the number of layers and neurons in DNNs increases rapidly, significant computational resources are needed to execute a learned DNN model....

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Veröffentlicht in:	IEEE transactions on parallel and distributed systems Jg. 32; H. 4; S. 799 - 814
Hauptverfasser:	Xu, Zichuan, Zhao, Liqian, Liang, Weifa, Rana, Omer F., Zhou, Pan, Xia, Qiufen, Xu, Wenzheng, Wu, Guowei
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.04.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	5G mobile communication Algorithms approximation and online algorithms Artificial intelligence Artificial neural networks Base stations Cloud computing Computation offloading Data centers Edge computing Electronic devices Heuristic algorithms Inference Inference offloading Mobile computing mobile edge clouds Mobile handsets Task analysis Video data
ISSN:	1045-9219, 1558-2183
Online-Zugang:	Volltext
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