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.

  '
difficulty: advanced
estimated_hours: 6-8
exports:
- tinytorch.quantization
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
name: Quantization
number: 17
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
type: optimization