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author:

Chen, Xing (Chen, Xing.) [1] | Zhang, Jianshan (Zhang, Jianshan.) [2] | Lin, Bing (Lin, Bing.) [3] | Chen, Zheyi (Chen, Zheyi.) [4] | Wolter, Katinka (Wolter, Katinka.) [5] | Min, Geyong (Min, Geyong.) [6]

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EI

Abstract:

Deep Neural Networks (DNNs) have become an essential and important supporting technology for smart Internet-of-Things (IoT) systems. Due to the high computational costs of large-scale DNNs, it might be infeasible to directly deploy them in energy-constrained IoT devices. Through offloading computation-intensive tasks to the cloud or edges, the computation offloading technology offers a feasible solution to execute DNNs. However, energy-efficient offloading for DNN based smart IoT systems with deadline constraints in the cloud-edge environments is still an open challenge. To address this challenge, we first design a new system energy consumption model, which takes into account the runtime, switching, and computing energy consumption of all participating servers (from both the cloud and edge) and IoT devices. Next, a novel energy-efficient offloading strategy based on a Self-adaptive Particle Swarm Optimization algorithm using the Genetic Algorithm operators (SPSO-GA) is proposed. This new strategy can efficiently make offloading decisions for DNN layers with layer partition operations, which can lessen the encoding dimension and improve the execution time of SPSO-GA. Simulation results demonstrate that the proposed strategy can significantly reduce energy consumption compared to other classic methods. © 1990-2012 IEEE.

Keyword:

Deep neural networks Energy efficiency Energy utilization Genetic algorithms Green computing Internet of things Mathematical operators Particle swarm optimization (PSO)

Community:

  • [ 1 ] [Chen, Xing]College of Mathematics and Computer Science, Fuzhou University, Fuzhou, China
  • [ 2 ] [Zhang, Jianshan]College of Mathematics and Computer Science, Fuzhou University, Fuzhou, China
  • [ 3 ] [Lin, Bing]College of Physics and Energy, Fujian Provincial Key Laboratory of Quantum Manipulation and New Energy Materials, Fujian Normal University, Fuzhou, China
  • [ 4 ] [Chen, Zheyi]Department of Computer Science, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom
  • [ 5 ] [Wolter, Katinka]Institut für Informatik, Freie Universität Berlin, Berlin, Germany
  • [ 6 ] [Min, Geyong]College of Mathematics and Computer Science, Fuzhou University, Fuzhou, China

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Source :

IEEE Transactions on Parallel and Distributed Systems

ISSN: 1045-9219

Year: 2022

Issue: 3

Volume: 33

Page: 683-697

5 . 3

JCR@2022

5 . 6 0 0

JCR@2023

ESI HC Threshold:61

JCR Journal Grade:1

CAS Journal Grade:2

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 171

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 1

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