🧠 NeuralEngineOptimizer

Production-ready Apple Silicon Neural Engine optimization for AI model inference

🚀 Overview

NeuralEngineOptimizer is a high-performance framework that unlocks the full potential of Apple's Neural Engine for AI inference. It provides:

• ⚡ Ultra-fast inference: Responses in under 2 seconds
• 🔒 Complete privacy: All processing happens locally
• 📊 Real-time monitoring: Beautiful Streamlit dashboard
• 🔧 Easy configuration: Simple YAML-based setup
• 🛡️ Enterprise-grade security: Input validation and rate limiting
• 📝 Structured logging: Production-ready logging system

📋 Requirements

• Apple M3 iMac or MacBook (or other Apple Silicon Mac)
• 16GB+ unified memory recommended
• Python 3.8+
• MLX framework: pip install mlx-lm
• Model storage: Compatible with MLX models

🔧 Installation

# Clone the repository
git clone https://github.com/yourusername/NeuralEngineOptimizer.git
cd NeuralEngineOptimizer

# Install dependencies
pip install -r requirements.txt

# Run quick test to verify installation
./scripts/quick_test.sh

🚀 Quick Start

from src.m3_neural_engine import M3NeuralEngineMLX

# Initialize Neural Engine
ai = M3NeuralEngineMLX()

# Generate text
result = ai.neural_engine_generate("Explain quantum computing in simple terms")
print(f"🤖 {result['response']}")
print(f"⚡ Response time: {result['processing_time']:.2f}s")

📊 Dashboard

Launch the interactive dashboard:

./scripts/start_dashboard.sh

Then open your browser to: http://localhost:8501

🔌 Integration

NeuralEngineOptimizer is ready for integration with:

• n8n: Use as a local AI node in workflows
• Node-RED: Local AI processing capabilities
• Keyboard Maestro: Trigger AI responses with keyboard shortcuts
• BetterTouchTool: Use gestures to activate AI features

See the integration examples for detailed guides.

📚 Documentation

• Complete API Reference
• Configuration Guide
• Security Features
• Dashboard Guide
• Integration Guide
• Performance Optimization

🛠️ Project Structure

NeuralEngineOptimizer/
├── src/                   # Source code
│   ├── m3_neural_engine.py  # Main Neural Engine module
│   ├── logger.py          # Structured logging
│   ├── security.py        # Security & validation
│   └── dashboard.py       # Streamlit dashboard
├── docs/                  # Documentation
├── examples/              # Example usage
├── tests/                 # Test suite
├── scripts/               # Utility scripts
├── config.yaml            # Configuration file
└── requirements.txt       # Python dependencies

🤝 Contributing

Contributions are welcome! Please check out our Contributing Guide.

📄 License

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

🙏 Acknowledgements

• MLX Team for the amazing Apple Silicon optimization framework
• Streamlit for the dashboard framework
• Apple Neural Engine for the hardware acceleration

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Made with ❤️ for Apple Silicon