- Analyzed current TinyTorch foundation (modules 00-09)
- Identified compression opportunities in Dense/CNN parameters
- Ranked 4 compression techniques by educational value:
1. Magnitude-based pruning (★★★★★) - builds on weight matrices
2. Quantization FP32→INT8 (★★★★) - builds on tensor operations
3. Knowledge distillation (★★★★) - builds on training pipeline
4. Structured pruning (★★★) - builds on architecture design
Educational progression:
- Step 1: Parameter analysis and model size understanding
- Step 2: Weight pruning with sparsity visualization
- Step 3: Quantization experiments with bit-width trade-offs
- Step 4: Teacher-student training with distillation loss
- Step 5: Neuron removal and architecture modification
- Step 6: Comprehensive technique comparison
Real-world connections:
- Mobile AI deployment constraints
- Production ML system optimization
- Research frontiers in model compression
Perfect foundation for modules 11-13 (kernels, benchmarking, MLOps)
