Channel Pruning
Surgically remove redundant neural channels to create ultra-lean, specialized models.
Structured Pruning
Unlike weight pruning (zeroing out individual weights), channel pruning removes entire convolutional filters. This results in direct speedups on standard hardware without needing specialized sparse-matrix engines.
Magnitude-based
Removes channels with the smallest weight norms.
Sensitivity-driven
Uses Taylor expansion to predict accuracy impact.
Iterative Refine
Prune, fine-tune, repeat for 0% accuracy loss.
Usage Example
edge-ai compress ./resnet50.pt --method prune --ratio 0.5
This removes 50% of redundant channels in the model architecture.
Why Prune?
Pruning is best for models that were over-engineered for simple tasks. A ResNet50 trained on 10 classes can usually be pruned by 80% with no loss in performance.