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Abstract ▼ AI-Helper

Recently, embedded Graphics Processing Units (GPUs) have started to support OpenCL framework which enables general-purpose computing on a GPU (GPGPU). However, the current embedded GPUs usually have a limited number of cores. To achieve better performance in such a system, it is essential to utilize...

Recently, embedded Graphics Processing Units (GPUs) have started to support OpenCL framework which enables general-purpose computing on a GPU (GPGPU). However, the current embedded GPUs usually have a limited number of cores. To achieve better performance in such a system, it is essential to utilize both the multi-core CPU and the GPU at the same time. On the other hand, face recognition is becoming popular in many applications, demanding more computation for higher recognition accuracy in real-time. In this paper, we present an efficient Local Binary Pattern (LBP) based face recognizer implemented using OpenCL on an embedded GPU. We explored different parallelization strategies exploiting both task-parallelism and data-parallelism on a heterogeneous embedded system, Exynos 5250: a system with a dual-core ARM Cortex-A15 CPU and a quad-core ARM Mali GPU. The experimental results show 7.5 times speedup compared to a single threaded CPU execution, and 7.0 times smaller energy consumption. To the best of our knowledge, this is the first work that presents the acceleration of LBP-based face recognition using OpenCL, and more importantly the first work that reports the performance of Mali GPU as a GPGPU using OpenCL.

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