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 digitalsignalprocessor(DSP) . The experimentalresults 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 requirementof low power consumption.