Noise Aware Power Adaptive Partitioned Deep Networks for Mobile Visual Assist Platforms

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Due to limitations in both compute power and battery capacity, many use cases for mobile devices often involve the offload of some portion of a task from the mobile platform to edge or cloud servers. Where there is flexibility in either the degree of offload or the nature of the communication to the remote device, there can be substantial tradeoffs between the amount of energy consumed by the mobile device for a given performance or quality of service (QoS) target. We investigate these tradeoffs in the specific case of deep neural networks (DNNs), which are known to both have noise tolerant properties and present many partitioning options for what portion of a task is computed locally versus remotely, and we present a scheme that exploits DNN QoS tolerance under reduced transmission fidelity to reduce mobile device power requirements. We characterize the error robustness of several networks as a function of cut depth, showing that resilience decreases as a function of layers and the degree of mismatch between training and operational noise levels, and develop an adaptive technique for run-time selection of an appropriate model, offload point, and transmission power level for a given noise environment.

Original languageEnglish (US)
Title of host publicationProceedings - 31st IEEE International System on Chip Conference, SOCC 2018
EditorsMassimo Alioto, Karan Bhatia, Mircea Stan, Ramalingam Sridhar, Helen Li
PublisherIEEE Computer Society
Pages284-289
Number of pages6
ISBN (Electronic)9781538614907
DOIs
StatePublished - Jan 17 2019
Event31st IEEE International System on Chip Conference, SOCC 2018 - Arlington, United States
Duration: Sep 4 2018Sep 7 2018

Publication series

NameInternational System on Chip Conference
Volume2018-September
ISSN (Print)2164-1676
ISSN (Electronic)2164-1706

Conference

Conference31st IEEE International System on Chip Conference, SOCC 2018
CountryUnited States
CityArlington
Period9/4/189/7/18

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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  • Cite this

    Zientara, P. A., Sampson, J., & Narayanan, V. (2019). Noise Aware Power Adaptive Partitioned Deep Networks for Mobile Visual Assist Platforms. In M. Alioto, K. Bhatia, M. Stan, R. Sridhar, & H. Li (Eds.), Proceedings - 31st IEEE International System on Chip Conference, SOCC 2018 (pp. 284-289). [8618580] (International System on Chip Conference; Vol. 2018-September). IEEE Computer Society. https://doi.org/10.1109/SOCC.2018.8618580