  The model quantization technique of deep neural networks has garnered
significant attention and has proven to be highly useful in compressing model
size, reducing computation costs, and accelerating inference. Many researchers
employ fake quantization for analyzing or training the quantization process.
However, fake quantization is not the final form for deployment, and there
exists a gap between the academic setting and real-world deployment.
Additionally, the inclusion of additional computation with scale and zero-point
makes deployment a challenging task. In this study, we first analyze why the
combination of quantization and dequantization is used to train the model and
draw the conclusion that fake quantization research is reasonable due to the
disappearance of weight gradients and the ability to approximate between fake
and real quantization. Secondly, we propose ISQuant as a solution for deploying
8-bit models. ISQuant is fast and easy to use for most 8-bit models, requiring
fewer parameters and less computation. ISQuant also inherits the advantages of
SQuant, such as not requiring training data and being very fast at the first
level of quantization. Finally We conduct some experiments and found the
results is acceptable.our code is available at https://github.com/
