Pruning

Description

Pruning is a model optimization technique used to reduce the size and computational complexity of a trained model while maintaining accuracy. This is achieved by identifying and removing redundant or less important parameters (weights) or structures (neurons, channels, or layers).

Pruning is particularly useful for deploying complex AI models on resource-constrained devices like edge devices.