@inproceedings{2a81193e01ae478abba41fca42ec269d,
title = "Real-time face detection in Full HD images exploiting both embedded CPU and GPU",
abstract = "CPU-GPU heterogeneous systems have become a mainstream platform in both server and embedded domains with ever increasing demand for powerful accelerator. In this paper, we present parallelization techniques that exploit both data and task parallelism of LBP based face detection algorithm on an embedded heterogeneous platform. By running tasks in a pipelined parallel way on multicore CPUs and by offloading a data-parallel task to a GPU, we could successfully achieve 29 fps for Full HD inputs on Tegra K1 platform where quad-core Cortex-A15 CPU and CUDA supported 192-core GPU are integrated. This corresponds to 5.54x speedup over a sequential version and 1.69x speedup compared to the GPU-only implementations.",
keywords = "CPU-GPU heterogeneous platform, Face detection, Tegra K1, data-parallel, task-parallel",
author = "Chanyoung Oh and Saehanseul Yi and Youngmin Yi",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; IEEE International Conference on Multimedia and Expo, ICME 2015 ; Conference date: 29-06-2015 Through 03-07-2015",
year = "2015",
month = aug,
day = "4",
doi = "10.1109/ICME.2015.7177522",
language = "English",
series = "Proceedings - IEEE International Conference on Multimedia and Expo",
publisher = "IEEE Computer Society",
booktitle = "2015 IEEE International Conference on Multimedia and Expo, ICME 2015",
address = "United States",
}