DEMO: DeepMon - Building mobile GPU Deep learning models for continuous vision applications
Deep learning has revolutionized vision sensing applications in terms of accuracy comparing to other techniques. Its breakthrough comes from the ability to extract complex high level features directly from sensor data. However, deep learning models are still yet to be natively supported on mobile de...
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sg-smu-ink.sis_research-46742018-12-19T06:45:14Z DEMO: DeepMon - Building mobile GPU Deep learning models for continuous vision applications HUYNH, Loc Nguyen BALAN, Rajesh Krishna LEE, Youngki Deep learning has revolutionized vision sensing applications in terms of accuracy comparing to other techniques. Its breakthrough comes from the ability to extract complex high level features directly from sensor data. However, deep learning models are still yet to be natively supported on mobile devices due to high computational requirements. In this paper, we present DeepMon, a next generation of DeepSense [1] framework, to enable deep learning models on conventional mobile devices (e.g. Samsung Galaxy S7) for continuous vision sensing applications. Firstly, Deep-Mon exploits similarity between consecutive video frames for intermediate data caching within models to enhance inference latency. Secondly, DeepMon leverages approximation technique (e.g. Tucker decomposition) to build up approximated models with negligible impact on accuracy. Thirdly, DeepMon ofloads heavy computation onto integrated mobile GPU to significantly reduce execution time of the model. 2017-06-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/3672 info:doi/10.1145/3081333.3089331 https://ink.library.smu.edu.sg/context/sis_research/article/4674/viewcontent/p186_huynh.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Continuous vision Deep learning Mobile GPU Mobile sensing Hardware Systems Software Engineering |
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Continuous vision Deep learning Mobile GPU Mobile sensing Hardware Systems Software Engineering HUYNH, Loc Nguyen BALAN, Rajesh Krishna LEE, Youngki DEMO: DeepMon - Building mobile GPU Deep learning models for continuous vision applications |
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Deep learning has revolutionized vision sensing applications in terms of accuracy comparing to other techniques. Its breakthrough comes from the ability to extract complex high level features directly from sensor data. However, deep learning models are still yet to be natively supported on mobile devices due to high computational requirements. In this paper, we present DeepMon, a next generation of DeepSense [1] framework, to enable deep learning models on conventional mobile devices (e.g. Samsung Galaxy S7) for continuous vision sensing applications. Firstly, Deep-Mon exploits similarity between consecutive video frames for intermediate data caching within models to enhance inference latency. Secondly, DeepMon leverages approximation technique (e.g. Tucker decomposition) to build up approximated models with negligible impact on accuracy. Thirdly, DeepMon ofloads heavy computation onto integrated mobile GPU to significantly reduce execution time of the model. |
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text |
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HUYNH, Loc Nguyen BALAN, Rajesh Krishna LEE, Youngki |
author_facet |
HUYNH, Loc Nguyen BALAN, Rajesh Krishna LEE, Youngki |
author_sort |
HUYNH, Loc Nguyen |
title |
DEMO: DeepMon - Building mobile GPU Deep learning models for continuous vision applications |
title_short |
DEMO: DeepMon - Building mobile GPU Deep learning models for continuous vision applications |
title_full |
DEMO: DeepMon - Building mobile GPU Deep learning models for continuous vision applications |
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DEMO: DeepMon - Building mobile GPU Deep learning models for continuous vision applications |
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DEMO: DeepMon - Building mobile GPU Deep learning models for continuous vision applications |
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demo: deepmon - building mobile gpu deep learning models for continuous vision applications |
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Institutional Knowledge at Singapore Management University |
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2017 |
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https://ink.library.smu.edu.sg/sis_research/3672 https://ink.library.smu.edu.sg/context/sis_research/article/4674/viewcontent/p186_huynh.pdf |
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