AI Model Compression

Model compression reduces size and inference time by 10-100x while maintaining 90-98% accuracy. Essential for deploying AI on edge devices (smartphones, IoT, embedded systems) where compute/memory is limited.

Compression Techniques

1. Quantization

• Reduce precision: FP32 → FP16 or INT8
• Post-Training Quantization: No retraining, 2-4x speedup
• Quantization-Aware Training: Better accuracy, 4-8x speedup
• 1-2% accuracy drop, 4-8x smaller, 2-4x faster

2. Pruning

• Remove unimportant weights/neurons
• Unstructured: Prune individual weights
• Structured: Prune entire neurons/filters
• 50-90% weight reduction, 2-10x speedup
• 1-3% accuracy drop with fine-tuning

3. Knowledge Distillation

• Train small model (student) to mimic large model (teacher)
• Transfer "knowledge" not just predictions
• 10-100x smaller model, 90-98% of teacher accuracy
• Example: DistilBERT (40% smaller, 97% accuracy)

4. Architecture Optimization

• Efficient architectures: MobileNet, EfficientNet
• Depthwise separable convolutions
• Designed for mobile/edge from scratch

Tools

• TensorRT: NVIDIA's inference optimizer
• ONNX Runtime: Cross-platform inference
• TensorFlow Lite: Mobile deployment
• PyTorch Mobile: On-device PyTorch
• OpenVINO: Intel's inference toolkit

Results

• Quantization (INT8): 4x smaller, 2-4x faster, <1% accuracy drop
• Pruning (70%): 3x smaller, 2-5x faster, 1-2% accuracy drop
• Distillation: 10-100x smaller, 2-8% accuracy drop
• Combined: 10-100x compression possible

Case Study: Mobile Object Detection

• Original: YOLOv5, 45MB, 10 FPS on phone
• Optimized: Pruned + quantized + optimized
• Results:
  • Model size: 45MB → 5MB (-89%)
  • Inference: 10 FPS → 45 FPS (+350%)
  • Accuracy: 88% → 86% (-2.3%)
  • Battery usage: -60%