An iterative pruning plus quantization co-design compresses a U-Net for hyperspectral driving-scene segmentation to 1% of its parameters, enabling a 2.86x inference speed-up on an FPGA SoC with negligible accuracy loss.
Review the State-of-the-art Technologies of Semantic Segmentation Based on Deep Learning.Neurocomputing, 493:626–646, 2022
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Optimization of DNN-based HSI Segmentation FPGA-based SoC for ADS: A Practical Approach
An iterative pruning plus quantization co-design compresses a U-Net for hyperspectral driving-scene segmentation to 1% of its parameters, enabling a 2.86x inference speed-up on an FPGA SoC with negligible accuracy loss.