Before revision:  The effects of EDA tools, input size of the deep neural network , and the data arrangement of representation on the detection accuracy are explored. The Xilinx Zynq-7000 SoCs and Xilinx Zynq UltraScale+ MPSoCs are adopted to verify the proposed method, and the results show that the mean Average Precision (IoU=0.5) for 10 function blocks is as high as 97.72\% for YOLOv3 detector  .
After revision:  The effects of EDA tools, input size of the deep neural network used for function block detection is trained and validated with a specially crafted bitstream/image dataset. Experiments have confirmed high detection accuracy of the proposed function detection method, showing a 98.11\% of mean Average Precision (IoU=0.5) for 10 function blocks is as high as 97.72\% for YOLOv3 detector .