SoC FPGA Acceleration for Semantic Segmentation of Clouds in Satellite Images

During the past decades the reports produced by the satellites regarding the clouds became important for the weather prediction, the climate and lately for the generation and the distribution of the solar power. Image segmentation techniques produce these reports and they exploit convolutional neural networks (CNNs) to improve the accuracy results at the expense though of the requirements for execution time and hardware resources. Aiming at improving this trade-off the current work studies the design of a CNN model targeting the placement and the execution on a SoC FPGA while keeping the high accuracy. Moreover, it studies how to optimize the performance by distributing the execution of the CNN's functions among the SoC's IP cores and the SoC's logic. The results are validated by using the Vitis-AI framework to implement the proposed CNN on a Xilinx Zynq UltraScale+ MPSoC ZCU104 board.