Advances in the Neural Network Quantization: A Comprehensive Review

作者：Wei Lu, Zhong Ma, Chaojie Yang, Qin Yao · 发表于：Applied Sciences · 年份：2024 · DOI：10.3390/app14177445 · 被引用次数：52 · 研究领域：Advanced Neural Network Applications、Brain Tumor Detection and Classification、Advanced Image and Video Retrieval Techniques

Artificial intelligence technologies based on deep convolutional neural networks and large language models have made significant breakthroughs in many tasks, such as image recognition, target detection, semantic segmentation, and natural language processing, but also face a conflict between the high computational capacity of the algorithms and limited deployment resources. Quantization, which converts floating-point neural networks into low-bit-width integer networks, is an important and essential technique for efficient deployment and cost reduction in edge computing. This paper analyzes various existing quantization methods, showcases the deployment accuracy of advanced techniques, and discusses the future challenges and trends in this domain.