Energy-Aware real-time face recognition system on mobile CPU-GPU platform

  • Authors:

  • Yi-Chu Wang;Bryan Donyanavard;Kwang-Ting (Tim) Cheng

  • Affiliations:

  • Dept. of Electrical and Computer Engineering, University of California, Santa Barbara, CA;Dept. of Electrical and Computer Engineering, University of California, Santa Barbara, CA;Dept. of Electrical and Computer Engineering, University of California, Santa Barbara, CA

  • Venue:
  • ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part II
  • Year:
  • 2010

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Abstract

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.