## 2 Related Work  
According to the degree of low precision, network quantization can be further divided into ultra-low precision quantization and 8-bit quantization. Ultra-low precision quantization refers to the use of 1 to 4-bit values to replace 32 floating-point parameters in the source network. A representative work of ultra-low precision quantization is the binary quantization network (BNN) [5]. When training the neural network, BNN quantizes the network weight parameters and activation values to 1 or \(-1\), so the exclusive OR (XOR) operation in the logical operation can be used to replace the matrix multiplication and addition operation of the original network, which greatly improves the operation efficiency. A three-valued quantization network (TWN) [6] quantizes ownership values to \(-1\), 0, and 1. Incremental quantization network (INQ) [7]: By iterative logarithmic transformation of weight parameters, the quantization error of each iteration is reduced, and finally the network performance degradation caused by the quantization process is reduced. However, due to the ultra-low precision quantization, the precision of the network performance is usually negatively affected. Moreover, the training error fluctuates violently in the training process. The most commonly used quantization method in the industry is 8-bit quantization. Its quantization strategy is relatively conservative. Using an 8-bit fixed-point number to store and calculate network parameters has little effect on network accuracy.  
Recently, to solve the problem of accuracy loss and instability in the simulation quantification, work such as that of [8] carried out the theoretical analysis of the convergence stability of quantitative training, and based on this, proposed the error-sensitive learning rate adjustment and the direction-adaptive gradient truncation method. [PERSON] proposed a fixed-point method using single instruction multiple data (SIMD) operations to improve model performance [9]. [PERSON] et al. established a unified 8-bit (Int8) training framework for general convolutional neural networks, which can accurately and efficiently train various networks and tasks. A direction-sensitive gradient clipping technique to reduce the gradient direction deviation and a deviation back learning rate scaling technique to avoid illegal gradient updating along the wrong direction were also proposed [10]. [PERSON] proposed a quantization scheme, which only depends on an integer algorithm to approximate the floating-point calculation in neural networks. The training the of simulation quantization effect helps to recover the accuracy of the model to almost the same level as the original data [11].  
Some well-known methods of 8-bit quantization are introduced above, but recently there have been some novel quantization methods that can be referred to and compared. [PERSON] proposes an optimization method based on the idea of quantization compensation [12]. Compared with their work, we design a new loss function to compensate for clipping loss, which can avoid model bias. [PERSON] proposed a clipping method based on learning parameters to quantize the network parameters during the training phase [13], while our method performs clip processing before training and then quantifies the inference, which is more efficient and has less final accuracy loss. [PERSON] also proposes a quantization method for adaptive models [14], but the author's method of improving model efficiency by removing the fully connected layer leads to a large loss of information, while our processing of the fully connected layer using SVD can refine the effective information with higher accuracy. [PERSON]'s method also quantifies by weight intervals [15]. However, the author's method was only validated on shallower networks such as VGG, and its processing of only valid bits may lead to accuracy loss in deeper networks.