  At present, the quantification methods of neural network models are mainly
divided into post-training quantization (PTQ) and quantization aware training
(QAT). Post-training quantization only need a small part of the data to
complete the quantification process, but the performance of its quantitative
model is not as good as the quantization aware training. This paper presents a
novel quantification method called Attention Round. This method gives
parameters w the opportunity to be mapped to all possible quantized values,
rather than just the two quantized values nearby w in the process of
quantization. The probability of being mapped to different quantified values is
negatively correlated with the distance between the quantified values and w,
and decay with a Gaussian function. In addition, this paper uses the lossy
coding length as a measure to assign bit widths to the different layers of the
model to solve the problem of mixed precision quantization, which effectively
avoids to solve combinatorial optimization problem. This paper also performs
quantitative experiments on different models, the results confirm the
effectiveness of the proposed method. For ResNet18 and MobileNetV2, the
post-training quantization proposed in this paper only require 1,024 training
data and 10 minutes to complete the quantization process, which can achieve
quantization performance on par with quantization aware training.
