Post Training Quantization after Neural Network

In recent years, neural network deployment to the target environment is considered a challenging task especially because of heavy burden of hardware requirements that DNN models lay on computation capabilities and power consumption. various methods based on post-training quantification are powerful tools to accelerate the optimization and perfection of deep neural networks. In case of low power edge devices, such as GNA - neural coprocessor, quantization becomes the only way to make the deployment possible. This article summarizes the research and improvement of neural network models and parameters of various methods in post-training quantization. Additionally, this article analyzes different methods and classifies them. Firstly, this article reviews the publications of post-training quantitative literature in recent years, analyzes the most effective cutting-edge algorithms in the field of deep neural networks. Secondly, advanced researches based on the AdaQuant algorithm, integer quantization analysis, and data-free quantization are discussed. It can reduce the model size and simplify the computation complexity. Compared to the state-of-the-art post-training quantization methods, experimental results show that our proposed method achieves superior performance on image classification, semantic segmentation, and object detection with minor overhead. Moreover, the model accuracy and development potential of post-training quantization in the field of deep neural networks are discussed further. The analysis shows the characteristics of different algorithms and the corresponding changed networks. The characteristics of the model affected by the parameters, and the future research directions and difficulties faced by the post-training quantization method and technology are covered in this article as well.