Weight Equalizing Shift Scaler-Coupled Post-training Quantization

Understand

Post-training, layer-wise quantization is preferable because it is free from retraining and is hardware-friendly.

• Nevertheless, accuracy degradation has occurred when a neural network model has a big difference of per-out-channel weight ranges.
• In particular, the MobileNet family has a tragedy drop in top-1 accuracy from 70.60% ~ 71.87% to 0.1% on the ImageNet dataset after 8-bit weight quantization.
• To mitigate this significant accuracy reduction, we propose a new weight equalizing shift scaler, i.e.