Data driven adaptation for QoS aware embedded vision systems

Chris S. Lee, Kevin M. Irick, John Sampson, Vijaykrishnan Narayanan

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

Abstract

We present a data driven method for high efficiency in a neuro-inspired vision pipeline. Our goal is to reduce low-utility computation arising from duplicated processing. In this paper, we examine two forms of redundant information in image data, spatiotemporal redundancy and channel redundancy. To maximize efficiency, the paper presents a dynamic, configurable approach that limits the computational cost of hardware by reusing previous results and sharing data paths. Our technique reduces redundant computation from both spatiotemporal and channel redundancy.

Original languageEnglish (US)
Title of host publication2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages69-72
Number of pages4
ISBN (Electronic)9781479970889
DOIs
StatePublished - Feb 5 2014
Event2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014 - Atlanta, United States
Duration: Dec 3 2014Dec 5 2014

Publication series

Name2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014

Other

Other2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014
CountryUnited States
CityAtlanta
Period12/3/1412/5/14

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Information Systems

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    Lee, C. S., Irick, K. M., Sampson, J., & Narayanan, V. (2014). Data driven adaptation for QoS aware embedded vision systems. In 2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014 (pp. 69-72). [7032080] (2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GlobalSIP.2014.7032080