Edge AI Engineering

Overview

A comprehensive guide to Edge AI Engineering, focusing on the deployment of AI models on edge devices. It covers fundamental concepts, practical techniques, and best practices for implementing efficient and effective Edge AI solutions.

Table of Contents

• Introduction to Edge AI
• Edge AI Architectures
• Model Optimization Techniques
• Hardware Acceleration
• Edge Deployment Strategies
• Real-Time Processing
• Privacy and Security
• Edge AI Frameworks
• Benchmarking and Performance
• Getting Started
• Contributing
• License

Introduction to Edge AI

• What is Edge AI?
• Benefits and Use Cases
• Challenges in Edge AI

Edge AI Architectures

• Cloud-Edge-Device Paradigm
• Edge-Only Architectures
• Hybrid Approaches

Model Optimization Techniques

• Quantization
• Pruning
• Knowledge Distillation
• Neural Architecture Search

Hardware Acceleration

• GPU Acceleration
• FPGA Solutions
• ASIC Accelerators
• NPU Integration

Edge Deployment Strategies

• Containerization
• Over-the-Air Updates
• Edge Orchestration

Real-Time Processing

• Latency Optimization
• Streaming Data Handling
• Event-Driven Architectures

Privacy and Security

• Data Protection at the Edge
• Secure Inference
• Device Hardening

Edge AI Frameworks

• TensorFlow Lite
• PyTorch Mobile
• ONNX Runtime
• OpenVINO

Benchmarking and Performance

• Metrics for Edge AI
• Benchmarking Tools
• Performance Optimization Strategies

Getting Started

1. Clone this repository:

   git clone https://github.com/afondiel/edge-ai-engineering.git
   

2. Navigate to the topic you're interested in within the docs folder.
3. Follow the guides and examples to deepen your understanding of Edge AI engineering.

Contributing

We welcome contributions from the community! Please see our CONTRIBUTING.md file for details on how to submit improvements or report issues.

License

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