Low-power Face Detection Acceleration System Based on the Zynq Platform
To address the issues of large size and high power consumption in CPU-and GPU-based convolutional neural network platforms,we designed and implemented a convolutional neural network-assisted face detection acceleration system based on the Zynq platform in this study.We adopted the YOLOv3-Tiny algorithm for the proposed system and used the WIDER FACE dataset for training.To improve the network efficiency,we utilized a layer-fusion technique for reducing the network depth and accelerating detection.Moreover,we employed an 8-bit integer quantization strategy to minimize memory access and resource consumption.We designed a reusable multichannel convolution computation module by leveraging the parallel computing capability of field-programmable gate arrays(FPGAs)on the ZynqXC7Z035 chip to reuse the digital signal processor(DSP).The experimental results showed that our designed acceleration system,which could achieve a real-time inference speed of 9.5 FPS,was 7.9 times faster than intel i7-8700CPU and consumed only 2.65 W of power,satisfying the performance requirement of low power consumption.
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