  Based on the model's resilience to computational noise, model quantization is
important for compressing models and improving computing speed. Existing
quantization techniques rely heavily on experience and "fine-tuning" skills. In
the majority of instances, the quantization model has a larger loss than a full
precision model. This study provides a methodology for acquiring a
mixed-precise quantization model with a lower loss than the full precision
model. In addition, the analysis demonstrates that, throughout the inference
process, the loss function is mostly affected by the noise of the layer inputs.
In particular, we will demonstrate that neural networks with massive identity
mappings are resistant to the quantization method. It is also difficult to
improve the performance of these networks using quantization.
