Using mobile GPU for general-purpose computing – a case study of face recognition on smartphones

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.

[1]  Jyrki Leskela,et al.  OpenCL embedded profile prototype in mobile device , 2009, 2009 IEEE Workshop on Signal Processing Systems.

[2]  Wen Gao,et al.  Hierarchical Ensemble of Global and Local Classifiers for Face Recognition , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[3]  Reiji Suda,et al.  Power Efficient Large Matrices Multiplication by Load Scheduling on Multi-core and GPU Platform with CUDA , 2009, 2009 International Conference on Computational Science and Engineering.

[4]  Sungdae Cho,et al.  Implementation and optimization of image processing algorithms on handheld GPU , 2010, 2010 IEEE International Conference on Image Processing.

[5]  Hyeonjoon Moon,et al.  The FERET Evaluation Methodology for Face-Recognition Algorithms , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Naga K. Govindaraju,et al.  Fast computation of general Fourier Transforms on GPUS , 2008, 2008 IEEE International Conference on Multimedia and Expo.

[7]  Tomas Akenine-Möller,et al.  Graphics Processing Units for Handhelds , 2008, Proc. IEEE.

[8]  Dave Shreiner,et al.  The OpenGL ES 2.0 programming guide , 2008 .

[9]  Hyeonjoon Moon,et al.  The FERET evaluation methodology for face-recognition algorithms , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[10]  Tomas Akenine-Möller,et al.  iPACKMAN: high-quality, low-complexity texture compression for mobile phones , 2005, HWWS '05.

[11]  Bringing High-End Graphics to Handheld Devices , 2011 .