TY - GEN
T1 - Energy-aware real-time face recognition system on mobile CPU-GPU platform
AU - Wang, Yi Chu
AU - Donyanavard, Bryan
AU - Cheng, Kwang Ting
PY - 2012
Y1 - 2012
N2 - The Graphics Processor Unit (GPU) has expanded its role from an accelerator for rendering graphics into an efficient parallel processor for general purpose computing. The GPU, an indispensable component in desktop and server-class computers as well as game consoles, has also become an integrated component in handheld devices, such as smartphones. Since the handheld devices are mostly powered by battery, the mobile GPU is usually designed with an emphasis on low-power rather than on performance. In addition, the memory bus architecture of mobile devices is also quite different from those of desktops, servers, and game consoles. In this paper, we try to provide answers to the following two questions: (1) Can a mobile GPU be used as a powerful accelerator in the mobile platform for general purpose computing, similar to its role in the desktop and server platforms? (2) What is the role of a mobile GPU in energy-optimized real-time mobile applications? We use face recognition as an application driver which is a compute-intensive task and is a core process for several mobile applications. The experiments of our investigation were performed on an Nvidia Tegra development board which consists of a dual-core ARM Cortex A9 CPU and a Nvidia mobile GPU integrated in a SoC. The experiment results show that, utilizing the mobile GPU can achieve a 4.25x speedup in performance and 3.98x reduction in energy consumption, in comparison with a CPU-only implementation on the same platform.
AB - The Graphics Processor Unit (GPU) has expanded its role from an accelerator for rendering graphics into an efficient parallel processor for general purpose computing. The GPU, an indispensable component in desktop and server-class computers as well as game consoles, has also become an integrated component in handheld devices, such as smartphones. Since the handheld devices are mostly powered by battery, the mobile GPU is usually designed with an emphasis on low-power rather than on performance. In addition, the memory bus architecture of mobile devices is also quite different from those of desktops, servers, and game consoles. In this paper, we try to provide answers to the following two questions: (1) Can a mobile GPU be used as a powerful accelerator in the mobile platform for general purpose computing, similar to its role in the desktop and server platforms? (2) What is the role of a mobile GPU in energy-optimized real-time mobile applications? We use face recognition as an application driver which is a compute-intensive task and is a core process for several mobile applications. The experiments of our investigation were performed on an Nvidia Tegra development board which consists of a dual-core ARM Cortex A9 CPU and a Nvidia mobile GPU integrated in a SoC. The experiment results show that, utilizing the mobile GPU can achieve a 4.25x speedup in performance and 3.98x reduction in energy consumption, in comparison with a CPU-only implementation on the same platform.
UR - https://openalex.org/W65494498
UR - https://www.scopus.com/pages/publications/84871177770
U2 - 10.1007/978-3-642-35740-4_32
DO - 10.1007/978-3-642-35740-4_32
M3 - Conference Paper published in a book
SN - 9783642357398
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 411
EP - 422
BT - Trends and Topics in Computer Vision - ECCV 2010 Workshops, Revised Selected Papers
T2 - 11th European Conference on Computer Vision, ECCV 2010
Y2 - 10 September 2010 through 11 September 2010
ER -