Post-training quantization reduces U-Net memory by 4x with maintained or improved segmentation accuracy, and a genetic-algorithm approach trains LUT-based binary networks for 10-15 ns FPGA inference without DSP or BRAM.
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Quantization Effects of Artificial Neural Networks for Embedded Edge-Computing Applications
Post-training quantization reduces U-Net memory by 4x with maintained or improved segmentation accuracy, and a genetic-algorithm approach trains LUT-based binary networks for 10-15 ns FPGA inference without DSP or BRAM.