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Edge & On-Device AI

Running AI where the data is — on phones, sensors, and microcontrollers — without a round trip to the cloud.

Edge & On-Device AI is one of the core areas in the AI University map of AI. Explore the diagram, then dive into each topic — every subtopic grows into its own deep-dive over time.

flowchart LR
  BIG[Large model] --> COMP[Quantize / distill] --> SMALL[Small model]
  SMALL --> DEV[[On-device runtime]] --> ACT[/Instant, private result/]

Key topics

  • Why on-device

    Latency, privacy, offline use, and cost — the case for local inference.

  • Model compression

    Quantization, pruning, and knowledge distillation to shrink models.

  • Efficient architectures

    MobileNets, small language models, and hardware-aware design.

  • On-device runtimes

    Core ML, TensorFlow Lite, ONNX Runtime, and the NPUs in modern chips.

  • TinyML

    Machine learning on microcontrollers with kilobytes of memory.

  • Hybrid edge-cloud

    Splitting work between device and server for the best of both.

AI Hardware & Compute · Data & MLOps · Computer Vision

Learn this properly

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