FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer

@inproceedings{Lin2022FQViTPQ,
  title={FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer},
  author={Yang Lin and Tianyu Zhang and Peiqin Sun and Zheng Li and Shuchang Zhou},
  booktitle={IJCAI},
  year={2022}
}
Network quantization significantly reduces model inference complexity and has been widely used in real-world deployments. However, most existing quantization methods have been developed mainly on Convolutional Neural Networks (CNNs), and suffer severe degradation when applied to fully quantized vision transformers. In this work, we demonstrate that many of these difficulties arise because of serious inter-channel variation in LayerNorm inputs, and present, Power-of-Two Factor (PTF), a… 

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