COVID-19 is still disrupting many parts of the world. A rapid and accurate diagnosis solution is needed to combat the pandemic. As a part of the AIRBiS(AI-Enabled Real-time Pneumonia Detection Bio-medical System), this work conduct hardware acceleration to speed up the diagnosis. We found that more than 90% of the current diagnosis time is spent on the convolution function and have conducted three methods to speed up the convolution operations. Firstly, by applying the Winograd algorithm on convolution layers, the multiplication operations of the matrices can be decreased, which speeds up the calculation. The next step is to improve the data exchange speed between the FPGA and CPU by replacing the normal buffer with LineBuffer. We also tried to improve the calculation speed by quantization, reducing the number of bits used for the filter and the input image. The FPGA board we used for this research is ZCU102. The application used for high-level synthesis is Xilinx SDSoC 2019.1. Using the mentioned approaches, we improved the inference speed from 106ms to 22.2ms per image.