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AI & Engineering Academics›🌲 AI Forest›Lessons›Edge AI - Running Models on Phones, Browsers, and IoT Devices
📱
AI Forest • Advanced⏱️ 17 min read

Edge AI - Running Models on Phones, Browsers, and IoT Devices

Public lesson preview

Explore model compression, on-device inference, and the hardware accelerators powering AI at the edge - from mobile phones to web browsers.

Programme
AI Forest
Level
Advanced
Reading time
17 min read

Learning objectives

By the end of this lesson, you will understand:

  • ✓Why Edge Over Cloud?
  • ✓Model Compression Techniques
  • ✓ONNX: The Universal Format
  • ✓Platform-Specific Runtimes
  • ✓On-Device Large Language Models

Lesson outline

  1. Why Edge Over Cloud?
  2. Model Compression Techniques
  3. Quantisation
  4. Pruning
  5. Knowledge Distillation
  6. ONNX: The Universal Format
  7. Platform-Specific Runtimes
  8. TensorFlow Lite (Mobile / IoT)
  9. Core ML (Apple Ecosystem)
  10. WebAssembly + ONNX Runtime Web
  11. On-Device Large Language Models
  12. Hardware Accelerators at the Edge
  13. Real-World Edge AI Applications
  14. 📚 Further Reading

Introductory preview

Not every prediction should travel to the cloud and back. Edge AI runs models directly on the device - your phone, your browser, a camera, a car. The benefits are compelling: zero network latency, full privacy (data never leaves the device), offline capability, and dramatically lower serving costs. The challenge? You need a model that fits in megabytes, runs on limited hardware, and still delivers useful accuracy.

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