Edge AI Optimization Tools

A collection of Python utilities for optimizing and deploying AI models on edge devices.

Features

• Model quantization using PyTorch
• Weight pruning for model compression
• Knowledge distillation framework
• ONNX export with optimization
• Performance benchmarking tools

Requirements

torch>=1.9.0
onnx>=1.10.0
onnxruntime>=1.8.0
numpy>=1.19.0

Installation

git clone https://github.com/yourusername/edge-ai-optimization
cd edge-ai-optimization
pip install -r requirements.txt

Usage

Quantization

Reduces model precision to decrease size while maintaining accuracy:

from edge_optimization import quantize_model

quantized_model = quantize_model(your_model, calibration_data)

Pruning

Removes unnecessary weights to reduce model size:

from edge_optimization import prune_model

pruned_model = prune_model(your_model, amount=0.3)  # Removes 30% of weights

Knowledge Distillation

Trains a smaller student model using a larger teacher model:

distillation_loss = DistillationLoss(temperature=3.0)
loss = distillation_loss(student_logits, teacher_logits)

ONNX Export

Exports and optimizes models for edge deployment:

from edge_optimization import optimize_for_edge

optimize_for_edge(model, sample_input, "model.onnx")

Benchmarking

Measures model performance metrics:

metrics = benchmark_model(model, test_data, device='cuda')
print(f"FPS: {metrics['fps']}")

Performance Considerations

• Quantization typically reduces model size by 75% with minimal accuracy loss
• Pruning can reduce model size by 30-50% depending on architecture
• ONNX optimization can improve inference speed by 20-40%
• Consider batch size and input dimensions for optimal performance

Known Limitations

• Quantization requires calibration data for best results
• Pruning may affect model accuracy on complex tasks
• ONNX optimization is model-architecture dependent
• GPU required for maximum performance benefits

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Citation

If you use this code in your research, please cite:

@software{edge_ai_optimization,
  author = {dewitt4},
  title = {Edge AI Optimization Tools},
  year = {2024},
  url = {https://github.com/dewitt4/edge-ai-optimization}
}