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Local Distributed Mobile Computing System for Deep Neural Networks

Mao, Jiachen (2017) Local Distributed Mobile Computing System for Deep Neural Networks. Master's Thesis, University of Pittsburgh. (Unpublished)

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Nowadays, Deep Neural Networks (DNN) are emerging as an excellent candidate in many ap- plications (e.g., image classification, object detection and natural language processing). Though ubiquitously utilized in many fields, DNN models are generally hard to be deployed on resource-constrained devices (e.g., mobile devices). In the prior arts, the research topics mainly focus on client-server computing paradigm or DNN model compression, which, respectively, ask for either outside infrastructure support or special iterative training phases. In this work, I propose a lo- cal distributed mobile computing system for the testing phase of DNNs called MDNN, short for Mobile Deep Neural Network. MDNN partitions already trained DNN models onto several mo- bile devices with the same local wireless network to accelerate DNN computations by alleviating device-level computing cost and memory usage. Two model partition schemes are also designed to minimize non-parallel data delivery time, including both wakeup time and transmission time. Experimental results show that when the number of worker nodes increases from 2 to 4, MDNN can accelerate the DNN computation by 2.17-4.28. Besides the parallel execution, the performance speedup also partially comes from the reduction of the data delivery time, e.g., 30.02% w.r.t. con- ventional 2D-grids partition. Furthermore, a model compression using group lasso is utilized for simultaneously alleviating computing cost and transmission cost.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Mao, Jiachenjim35@pitt.edujim35
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairChen, Yiranyic52@pitt.eduyic52
Committee MemberLi, Haihal66@pitt.eduhal66
Committee MemberMao, Zhi-Hongmaozh@engr.pitt.eduzhm4
Committee MemberDickerson,
Date: 13 June 2017
Date Type: Publication
Defense Date: 3 April 2017
Approval Date: 13 June 2017
Submission Date: 3 April 2017
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 36
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Electrical and Computer Engineering
Degree: MS - Master of Science
Thesis Type: Master's Thesis
Refereed: Yes
Uncontrolled Keywords: Mobile Computing, Deep Neural Network
Date Deposited: 13 Jun 2017 15:40
Last Modified: 13 Jun 2017 15:40


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