DeepThings: Distributed Adaptive Deep Learning Inference on Resource-Constrained IoT Edge Clusters

Edge computing has emerged as a trend to improve scalability, overhead, and privacy by processing large-scale data, e.g., in deep learning applications locally at the source. In IoT networks, edge devices are characterized by tight resource constraints and often dynamic nature of data sources, where...

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Veröffentlicht in:	IEEE transactions on computer-aided design of integrated circuits and systems Jg. 37; H. 11; S. 2348 - 2359
Hauptverfasser:	Zhao, Zhuoran, Barijough, Kamyar Mirzazad, Gerstlauer, Andreas
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.11.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Accuracy Artificial neural networks Clusters Constraints Data sources Deep learning Distributed databases distributed inference Dynamic scheduling Edge computing Electronic devices Embedded systems Footprints Inference Internet of Things Logic gates Neural networks Parallel processing Runtime Scheduling Task analysis
ISSN:	0278-0070, 1937-4151
Online-Zugang:	Volltext
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