mar 24, 2018 - QAT: Quantization and training of neural networks for efficient integer-arithmetic-only inference, CVPR

Description:

QAT employs the affine mapping of integers to real values with two constant parameters: Scale and Zeropoint. It first subtracts the Zero-point parameter from data (weights/activation), then divides the data by scale parameter, and finally getting the quantized results with rounding operation and affine mapping.

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