name: Quantization
number: 17
type: optimization
difficulty: advanced
estimated_hours: 6-8

description: |
  Precision optimization through INT8 quantization. Students learn to reduce model size
  and accelerate inference by using lower precision arithmetic while maintaining accuracy.
  Especially powerful for CNN convolutions and edge deployment.

learning_objectives:
  - Understand precision vs performance trade-offs
  - Implement INT8 quantization for neural networks  
  - Build calibration-based quantization systems
  - Optimize CNN inference for mobile deployment

prerequisites:
  - Module 09: Spatial (CNNs)
  - Module 16: Acceleration

skills_developed:
  - Quantization techniques and mathematics
  - Post-training optimization strategies
  - Hardware-aware optimization
  - Mobile and edge deployment patterns

exports:
  - tinytorch.quantization