## 1 Introduction  
With the development of big data and computer hardware, it has become possible to train large deep neural networks (DNNs). Therefore, deep neural networks have been widely studied in recent years and applied to face recognition, image classification, autonomous driving, natural language processing, and other artificial intelligence fields. Although deep neural networks have excellent performance and have been able to lead humans in some tasks, most deep neural network models have the disadvantages of large size (large space consumption for model storage) and high computational cost. The excellent performance of deep neural networks comes at the cost of high computational complexity and huge memory consumption. As the network grows in size, the number of parameters in the network grows and the amount of computation becomes larger. This leads to two core problems:  
1. It is difficult to deploy it in some resource-constrained devices, which greatly limits the application scenarios of neural networks. For example, in scenarios such as the Internet of Things (IoT), embedded systems not only lack rich computing and storage resources but also have a very urgent need for low power consumption. 2. It is difficult to apply in some industrial scenarios with high real-time requirements, such as autonomous driving. These technologies require extremely high real-time network inference; otherwise, there will be safety hazards. Therefore, a variety of deep neural network compression and acceleration techniques have emerged to try to remove redundancy in the network while ensuring network accuracy, i.e., to find a good tradeoff between network performance and computational cost. In general, model compression and acceleration of deep neural networks have both industrial application and academic research implications: In terms of application, the technology can make it easier to land artificial intelligence applications. Current deep neural networks are difficult to use in scenario-constrained environments due to their large number of parameters and computations, and studying how to compress and accelerate deep neural networks will help solve such problems. In theoretical research, the technique can help people to better understand deep neural networks. Various existing deep neural networks all suffer from information redundancy. By using model compression and acceleration algorithms, the redundancy in the network can be removed and the part of the network that really works can be uncovered. The study of this part will help people directly design deep neural networks that are more efficient and have better generalization ability. Previous methods could be roughly divided into two categories. The first category is to design a lightweight network structure. Taking MobileNet [1], SqueezeNet [2] ShuffleNet [3], and DenseNet [4] as examples, the main idea of these methods is to design a more efficient network computing method to reduce network parameters without decreasing network performance; however, although the lightweight models could effectively reduce network parameters, their generalization abilities are partly sacrificed and perform more poorly than conventional models. For example, the lightweight model MobileNet-224 achieves 70.6% accuracy on the ImageNet classification, but a non-conventional model such as SENet obtains 83.8% accuracy. It motivates us to seek new mechanisms for achieving compressed models. The second class quantizes the weights and input of a CNN from a 32-bit float number to a lower-bit number. This paper mainly studies the method for quantification. This paper focuses on the study of 8-bit quantization with minimal accuracy loss. According to the traditional quantization method, the post-training quantization method based on clipping is proposed.