The DOI matches its Crossref record.
Using mobile GPU for general-purpose computing – a case study of face recognition on smartphones
Abstract
As GPU becomes an integrated component in handheld devices like smartphones, we have been investigating the opportunities and limitations of utilizing the ultra-low-power GPU in a mobile platform as a general-purpose accelerator, similar to its role in desktop and server platforms. The special focus of our investigation has been on mobile GPU's role for energy-optimized real-time applications running on battery-powered handheld devices. In this work, we use face recognition as an application driver for our study. Our implementations on a smartphone reveals that, utilizing the mobile GPU as a co-processor can achieve significant speedup in performance as well as substantial reduction in total energy consumption, in comparison with a mobile-CPU-only implementation on the same platform.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
High-end mobile platforms already contain GPUs that can be used for general purpose processing.
“High-end mobile platforms already contain GPUs that can be used for general purpose processing [47].”
From Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition (2016) · cites this paper as [47]
Citations
Cites
No references could be resolved yet.
Research works printed but not linked: 17
- 2008, Nvidia Compute Unified Device Architecture (CUDA) Programming Guide
- munshi, 2008, OpenGL ES 2 0 Programming Guide
- 10.1109/ICCV.2007.4409060 · DOI
- phillips, 0, The FERET Evaluation Methodology for Race-Recognition Algorithms, IEEE Transactions on Pattern Analysis and Machine Intelligence, 22, 1090, 10.1109/34.879790 · DOI · Paper page
- 2010, Open Handset Alliance Android Software Develop Kit 2 2
- sumanaweera, 2005, Medical image reconstruction with the FFT, GPU Gems 2, 765
- 10.1109/CSE.2009.488 · DOI
- brandon, 2008, Fast computation of general Fourier Transforms on GPUs, IEEE International Conference on Multimedia and Expo
- 10.1109/SIPS.2009.5336267 · DOI
- 2011, Bring High-End Graphics to Handheld Devices, nVidia Whitepaper
- 10.1109/JPROC.2008.917719 · DOI
- 10.1109/ICIP.2010.5651740 · DOI
- 0, OpenGL ES 2 0 specification
- 10.1145/1071866.1071877 · DOI
- 0, Qualcomm Inc
- 0, OMAP3 family of multimedia application processors
- 0, Open Computing Language (OpenCL) Specification
Cited by
Sorted by citations
Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition
Ordóñez, F., & Roggen, D. (2016) · Sensors · doi:10.3390/s16010115 · Cited by 2,761
“High-end mobile platforms already contain GPUs that can be used for general purpose processing [47].”
BackgroundMorales, F. J. O., & Roggen, D. (2016) · Proceedings of the 2016 ACM International Symposium on Wearable Computers · doi:10.1145/2971763.2971764 · Cited by 136
Metadata
- Authors
- Cheng, K.-T., & Wang, Y.-C.
- Affiliations
- University of California, Santa Barbara (US)
- ORCID
- Kwang-Ting Cheng
- Venue
- Proceedings of 2011 International Symposium on VLSI Design, Automation and Test
- Publisher
- IEEE
- Published
- 2011-04
- Pages
- 1-4
- OpenAlex
- W2146894470
- Cited by
- 91
- References
- 0
- Citations by year
- 2025: 1 · 2024: 3 · 2023: 1 · 2022: 1 · 2021: 5 · 2020: 3 · 2019: 1 · 2018: 4 · 2017: 12 · 2016: 14 · 2015: 16 · 2014: 18 · 2013: 9 · 2012: 2
- FWCI
- 6.18
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Symposium on VLSI Design, Automation and Test (publishedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Hardware and Architecture › Parallel Computing and Optimization Techniques
- Physical Sciences › Computer Science › Hardware and Architecture › Embedded Systems Design Techniques
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Green IT and Sustainability
- Keywords
- mobile GPUs, GPU computing, face recognition, smartphones, energy efficiency, low-power electronics, coprocessor, real-time applications, battery-powered devices, speedup
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
