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A Locally Distributed Mobile Computing Framework for DNN based Android Applications

Published: 16 September 2018 Publication History

Abstract

In recent years, with the development of deep neural network (DNN), more and more applications (e.g., image classification, target recognition and audio processing) are supported by it. However, the disadvantage of its own large model makes it difficult to apply on resource-constrained devices such as mobile devices. In order to solve this problem, the existing research and technology mainly focus on the DNN model compression and the segmentation migration of the model. The former is generally at the expense of reducing accuracy, and the segmentation of the model has no unified migration tool for the DNN model of different applications. In this work, we propose a universal neural network layer segmentation tool, which enables the trained DNN model to be migrated, and migrates the segmentation layer to the nodes in the current network in accordance with the dynamic optimal allocation algorithm proposed in this paper. The experimental results show that the tool can adapt to various neural networks with different structures and perform optimal allocation of layers through algorithm. When the number of working nodes increases from 1 to 5, this method can speed up DNN 2-2.5 times, and shows a good acceleration effect.

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cover image ACM Other conferences
Internetware '18: Proceedings of the 10th Asia-Pacific Symposium on Internetware
September 2018
167 pages
ISBN:9781450365901
DOI:10.1145/3275219
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

  • Institute of Software, Chinese Academy of Sciences
  • CCF: China Computer Federation

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 September 2018

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Author Tags

  1. Deep neural network
  2. Mobile computing
  3. Optimal allocation
  4. Segmentation

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  • Short-paper
  • Research
  • Refereed limited

Funding Sources

  • the National Key R&D Program of China,Natural Science Foundation of China,Guiding Project of Fujian Province under

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Internetware '18

Acceptance Rates

Internetware '18 Paper Acceptance Rate 20 of 26 submissions, 77%;
Overall Acceptance Rate 55 of 111 submissions, 50%

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Cited By

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  • (2024)Efficient Service Function Chain Placement over Heterogeneous Devices in Deviceless Edge Computing EnvironmentsIEEE Transactions on Computers10.1109/TC.2024.3475590(1-14)Online publication date: 2024
  • (2023)An Efficient and Robust Cloud-Based Deep Learning With Knowledge DistillationIEEE Transactions on Cloud Computing10.1109/TCC.2022.316012911:2(1733-1745)Online publication date: 1-Apr-2023
  • (2022)Distributed Training for Deep Learning Models On An Edge Computing Network Using Shielded Reinforcement Learning2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)10.1109/ICDCS54860.2022.00062(581-591)Online publication date: Jul-2022
  • (2022)Efficient Computer Vision on Edge Devices with Pipeline-Parallel Hierarchical Neural Networks2022 27th Asia and South Pacific Design Automation Conference (ASP-DAC)10.1109/ASP-DAC52403.2022.9712574(532-537)Online publication date: 17-Jan-2022
  • (2020)Distributed Video Analysis for Mobile Live Broadcasting Services2020 IEEE Wireless Communications and Networking Conference (WCNC)10.1109/WCNC45663.2020.9120783(1-6)Online publication date: May-2020
  • (2020)Convergence of Edge Computing and Deep Learning: A Comprehensive SurveyIEEE Communications Surveys & Tutorials10.1109/COMST.2020.297055022:2(869-904)Online publication date: Oct-2021

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