We present the implementation of four FPGA-accelerated convolutional neural network (CNN) models for onboard cloud detection in resource-constrained CubeSat missions, leveraging Xilinx’s Vitis AI (VAI) framework and deep learning processing unit (DPU), a programmable engine with preimplemented, parameterizable IP cores optimized for deep neural networks, on a Zynq UltraScale+ MPSoC. This study explores both pixel-wise (Pixel-Net and Kernel-Net) and image-wise (U-Net and Patch-Net) models to benchmark tradeoffs in accuracy, latency, and model complexity…
