11241

Parallelization and Optimization of Feature Detection Algorithms on Embedded GPU

Seung Heon Kang, Seung-Jae Lee, In Kyu Park
Department of Information and Communication Engineering, Inha University, Incheon 402-751, Korea
International Workshop on Advanced Image Technology (IWAIT’14), 2014

@article{kang2014parallelization,

   title={Parallelization and Optimization of Feature Detection Algorithms on Embedded GPU},

   author={Kang, Seung Heon and Lee, Seung-Jae and Park, In Kyu},

   year={2014}

}

Download Download (PDF)   View View   Source Source   

2072

views

In this paper, we parallelize and optimize the popular feature detection algorithms, i.e. SIFT and SURF, on the latest embedded GPU. Using conventional OpenGL shading language and recently developed OpenCL as the GPGPU software platforms, we compare the implementation efficiency and speed performance between each other as well as between GPU and CPU. Experimental result shows that implementation on OpenCL is more efficient but has comparable performance with OpenGL. Compared with the performance on the embedded CPU in the same application processor, the embedded GPU runs 4-5 times faster. Furthermore, we measure and compare the power consumption on each implementation, which shows that OpenCL consumes less energy than OpenGL.
No votes yet.
Please wait...

* * *

* * *

HGPU group © 2010-2024 hgpu.org

All rights belong to the respective authors

Contact us: