Model Compression — Making Deep Learning Fast and Efficient

Deep learning models are often over-parameterized for deployment. Model compression reduces model size and computation through pruning, quantization, and knowledge distillation — enabling deployment on edge devices while maintaining accuracy and reducing inference costs.

• Key point 1 — Structured pruning removes entire filters for direct speedup on standard hardware
• Key point 2 — INT8 quantization achieves 4x compression with minimal accuracy loss
• Key point 3 — Knowledge distillation transfers teacher knowledge to compact student models

"The best model is one that runs everywhere, not just in the lab."