Neural Network Compression

Neural network compression refers to the optimization techniques used to reduce the number of parameters and computational complexity of deep learning models, thereby improving their efficiency and reducing resource consumption. The primary goal is to achieve model miniaturization and acceleration while maintaining model performance, thus enhancing deployment flexibility and energy efficiency. Neural network compression has significant application value in resource-constrained environments such as mobile devices, embedded systems, and edge computing, effectively promoting the widespread use of artificial intelligence technologies.