An energy efficient hybrid FPGA-GPU based embedded platform to accelerate face recognition application
Santhosh Kumar Rethinagiri, Oscar Palomar, Javier Moreno, Osman Ünsal, Adrián Cristal · 2015
Nowadays face recognition application is widely used in various industries such as traffic, safety, medical engineering, etc. In this paper, we propose a power and energy efficient heterogeneous platform to accelerate face recognition applications. To achieve this efficiency, we propose a novel hybrid platform which consists of a Xilinx Zynq (ARM+FPGA) and an NVidia's Jetson TK1 (ARM+GPU) coupled with PCIe card. In this application, we optimized local binary pattern and eigenvalue based face detection and recognition in order to achieve a speedup of 69x when compared to sequential execution on the ARM core, 4.8x against Zynq platform (ARM+FPGA), 3.2x against NVidia platform (ARM+GPU) and 40% more energy efficient against sequential execution.