Neural Network Compression

Compressing neural networks refers to the use of various techniques to reduce the number of parameters and computational complexity of deep learning models, thereby achieving more efficient storage and runtime performance. The primary goal is to maintain the model's predictive accuracy while lowering resource consumption, enhancing deployment flexibility, and scalability. Neural network compression is of significant importance in resource-constrained environments such as mobile devices, embedded systems, and edge computing, as it can substantially improve the model's real-time performance and energy efficiency.