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.
Deep Learning in Medical Hyperspectral Images: A Review.Sensors, 22(24):9790, 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.