# Advances in the Neural Network Quantization: A Comprehensive Review
## 1 Introduction  
With the rapid development of artificial intelligence and machine learning technologies, deep learning models have achieved excellent results in many fields, such as target detection [1, 2, 3, 4], intelligent obstacle avoidance [5], semantic segmentation [6, 7, 8, 9], and situational awareness [10]. However, the high performance of these models often relies on a large number of parameters and complex computational processes, leading to significant challenges in computational resource and energy consumption in their practical applications. Existing deep learning models still face the shortcomings of requiring high storage, memory bandwidth, and computational power [11]. These shortcomings limit their use in resource-limited devices in many practical applications. Therefore, how to reduce model size, improve model running speed, and enable existing deep models to be applied to embedded platforms are key to solving the current challenges of deep learning applications. To address these challenges, neural network model quantization techniques have been extensively investigated in recent years to compress neural network models and accelerate their inference process by converting high-precision data to low-precision data [12, 13]. For example, quantizing 32-bit multiplication operations to 8-bit can reduce power consumption by about 94.59% (8-bit multiplication operations consume 0.2 pj, while 32-bit multiplication operations consume 3.7 pj), and the data transfer speed of the quantized model can be increased to 4 times that of the original model [14, 15]. Neural network model quantization is an important technology that needs to be solved in the field of artificial intelligence, and has urgent application needs and broad application prospects in the fields of embedded high-speed inference and large model compression. (1) There is a need for efficient computation of intelligent algorithms on the embedded devices. With the continuous progress of artificial intelligence technology, the application field of neural networks is also expanding. Due to the high computational complexity of intelligent algorithm models represented by neural networks, the contradiction between limited computational resources and the high computational demand of intelligent modelsseriously restricts the deployment of high-performance neural network models on end-side computing devices, which has become a bottleneck restricting the promotion and application of artificial intelligence technology [16; 17; 18]. To address this problem, there is an urgent need to carry out research on quantization techniques for neural network models to reduce data storage, data transmission, and computational power. Modern neural networks, while highly effective, often require substantial computational resources and memory, which can be prohibitive for deployment on edge devices with limited hardware capabilities. By reducing the precision of the model's weights and activations, quantization techniques aim to shrink the model size and enhance efficiency without significantly sacrificing performance. (2) Deployment Requirements for Large Language Models. In recent years, big models based on ultra-large-scale text data, represented by BERT [19] and GPT [20; 21], have flourished and made substantial progress in tasks such as natural language reasoning and human-computer dialogue, reaching or even surpassing the human level on some datasets. Among them, the most famous is the GPT series of models introduced by OpenAI, whose latest version has reached the scale of 100 billion parameters and is regarded as the most advanced language model. With the rapid increase in the scale of large models, the demand for GPU memory and arithmetic power is getting bigger and bigger, resulting in bottlenecks involving storage, access bandwidth, and computation speed; it is getting more and more difficult for high-performance servers to meet the demand for hardware resources of large models.