Pruning

Pruning is a model compression technique that systematically removes redundant or less important parameters from neural networks to reduce model size, computational requirements, and memory usage while maintaining acceptable performance levels. This process identifies and eliminates weights, neurons, or entire network structures based on importance criteria such as magnitude, gradient information, or contribution to model output.

Pruning enables deployment of large models on resource-constrained devices, reduces inference latency, and lowers energy consumption through strategic parameter elimination. The technique encompasses various approaches including structured pruning that removes entire channels or layers, unstructured pruning that eliminates individual weights, and gradual pruning that iteratively removes parameters during training. Advanced pruning methods incorporate techniques like lottery ticket hypothesis, magnitude-based pruning, and neural architecture search to optimize compression ratios while preserving model capabilities. Pruning serves as a fundamental optimization strategy for edge AI deployment, mobile applications, and efficient large-scale model serving.