Accelerating Mobile Audio Sensing Algorithms through On-Chip GPU Offloading
- Submitting institution
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University of Cambridge
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1867
- Type
- E - Conference contribution
- DOI
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10.1145/3081333.3081358
- Title of conference / published proceedings
- MobiSys 2017 - Proceedings of the 15th Annual International Conference on Mobile Systems, Applications, and Services
- First page
- 306
- Volume
- -
- Issue
- -
- ISSN
- -
- Open access status
- -
- Month of publication
- June
- Year of publication
- 2017
- URL
-
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- Supplementary information
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-
- Request cross-referral to
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- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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3
- Research group(s)
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-
- Citation count
- 4
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper was one of the first to show how on-device computation is effective and feasible. The unique counter-intuitive finding of this paper is that GPUs can be used efficiently for on device computation, despite being power hungry. The smart organization of audio inference allows the use of wearable devices' GPU and offer comparable performance to when computation is sent to cloud in terms of energy consumption while maintaining good responsiveness. The paper was the basis of an Invited Talk at the ARM Cambridge Research Summit in September 2016.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -