Name
Abstract | ||
|---|---|---|
Along with the inclusion of GPU cores within the same CPU die, the performance of Intel's processor-graphics has been significantly improved over earlier generation of integrated graphics. The need to efficiently harness the computational power of the GPU in the same CPU die is more than ever. This paper presents a highly optimized Haar-based face detector which efficiently exploits both CPU and GPU computing power on the latest Sandy Bridge processor. The classification procedure of Haar-based cascade detector is partitioned to two phases in order to leverage both thread level and data level parallelism in the GPU. The image downscaling and integral image calculation running in the CPU core can work with the GPU in parallel. Compared to CPU-alone implementation, the experiments show that our proposed GPU accelerated implementation achieves a 3.07x speedup with more than 50% power reduction on the latest Sandy Bridge processor. On the other hand, our implementation is also more efficient than the CUDA implementation on the NVidia GT430 card in terms of performance as well as power. In addition, our proposed method presents a general approach for task partitioning between CPU and GPU, thus being beneficial not only for face detection but also for other multimedia and computer vision techniques. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ICME.2012.121 | ICME |
Keywords | Field | DocType |
cpu die,cuda implementation,cpu cooperative accelaration,latest sandy bridge processor,face detection,gpu computing power,computational power,power reduction,proposed gpu,cpu-alone implementation,gpu core,modern processors,cpu core,computer architecture,computer vision,face recognition,instruction sets,feature extraction,multimedia,image classification,parallel processing,data level parallelism,thread level parallelism | Central processing unit,Computer science,CUDA,Instruction set,Parallel computing,Data parallelism,General-purpose computing on graphics processing units,Graphics processing unit,CPU shielding,Speedup | Conference |
Citations | PageRank | References |
3 | 0.39 | 4 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eric Li | 1 | 3 | 0.39 |
| Bin Wang | 2 | 95 | 11.84 |
| Liu Yang | 3 | 473 | 43.62 |
| Ya-ti Peng | 4 | 55 | 6.07 |
| Yangzhou Du | 5 | 169 | 13.85 |
| Yimin Zhang | 6 | 1536 | 130.17 |
| Yi-Jen Chiu | 7 | 9 | 3.40 |