Adaptive Edge AI 🤖

📋 Overview

Adaptive Edge AI is a project focused on optimizing and deploying deep learning models for edge devices through model compression and knowledge distillation techniques. This solution enables efficient AI model deployment on resource-constrained devices while maintaining high performance.

🌟 Key Features

• 🔄 Model Compression
• 📚 Knowledge Distillation
• 📱 Edge Device Optimization
• 📊 Adaptive Performance Scaling
• 🚀 ONNX Export Support

🏗️ Architecture

graph LR
A[Teacher Model] --> B[Knowledge Distillation]
B --> C[Student Model]
C --> D[Model Compression]
D --> E[Edge Deployment]

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🛠️ Installation

1. Clone the repository

git clone https://github.com/msadeqsirjani/adaptive_edge_ai.git

2. Create and activate virtual environment

python -m venv .venv
source .venv/bin/activate # Linux/Mac
# or
.venv\Scripts\activate # Windows

3. Install dependencies

pip install -r requirements.txt

📊 Performance Metrics

Model	Size	Accuracy	Inference Time
Teacher	500MB	95%	100ms
Student	50MB	92%	20ms
Compressed	10MB	90%	5ms

💻 Usage

Training the Teacher Model

python main.py --mode train_teacher --data_path data/

Knowledge Distillation

python main.py --mode distill --teacher_model best_teacher_model.pth

Model Compression

python main.py --mode compress --model student_model.pth

📁 Project Structure

adaptive_edge_ai/
├── data/ # Dataset directory (gitignored)
├── src/
│ ├── models/ # Model architectures
│ ├── optimization/ # Compression algorithms
│ ├── training/ # Training utilities
│ └── utils/ # Helper functions
├── outputs/ # Saved models & results
├── tests/ # Unit tests
├── requirements.txt # Dependencies
└── main.py # Entry point

📈 Results

Our compressed models achieve:

• 📉 90% size reduction
• ⚡ 20x faster inference
• 💪 Minimal accuracy loss

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

📄 License

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

📬 Contact

• Mohammad Sadegh Sirjani - @msadeqsirjani
• Email - m.sadeq.sirjani@gmail.com

🙏 Acknowledgments

• Thanks to relevant papers or projects
• Special thanks to contributors
• Inspired by related work

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