Mao, Jiachen
(2017)
Local Distributed Mobile Computing System for Deep Neural Networks.
Master's Thesis, University of Pittsburgh.
(Unpublished)
Abstract
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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Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
|
| 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 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/31183 |
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