Introduction: Making Models Faster and Smaller
Your neural network achieves 95% accuracy... but it's 500MB, takes 2 seconds per prediction, and requires a GPU. Production systems need fast, small, and efficient models.
Model optimization reduces size and latency while maintaining accuracy.
Key Techniques:
- Quantization (reduce precision)
- Pruning (remove weights)
- Knowledge distillation (teacher-student)
- Compilation (optimize computation graph)
Learning Objectives
- Understand model optimization trade-offs
- Apply quantization techniques
- Implement model pruning
- Use knowledge distillation
- Optimize inference with compilation
- Choose appropriate optimization for use case
1. Why Optimize?
The Optimization Trade-off
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2. Quantization
Idea: Use lower precision (INT8 instead of FP32) for weights and activations.
FP32 (32-bit float): Standard training precision INT8 (8-bit integer): 4x smaller, 2-4x faster inference
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3. Model Pruning
Idea: Remove unimportant weights (set to zero) to reduce model size.
Types:
- Unstructured pruning: Remove individual weights
- Structured pruning: Remove entire neurons/channels
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4. Knowledge Distillation
Idea: Train a small "student" model to mimic a large "teacher" model.
Process:
- Train large, accurate teacher model
- Use teacher's outputs as "soft targets"
- Train smaller student to match teacher
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5. Model Compilation
Idea: Optimize the computation graph for specific hardware.
Tools:
- TensorRT (NVIDIA GPUs)
- ONNX Runtime
- TensorFlow Lite (mobile/edge)
- Apache TVM
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6. Optimization Decision Guide
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Key Takeaways
✅ Quantization reduces precision (FP32 → INT8) for 4x smaller models
✅ Pruning removes unimportant weights for sparse models
✅ Knowledge distillation trains small students to mimic large teachers
✅ Compilation optimizes computation graph for hardware
✅ Trade-offs: Speed/size gains vs. accuracy loss
✅ Combine techniques for maximum optimization
What's Next?
Final lesson: Production Best Practices – security, reliability, scalability, and real-world deployment strategies!