The Gradient: Perspectives on AI

Daniel Situnayake: AI on the Edge

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Apr 6, 2023
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INSIGHT

Hardware Forces Pragmatic AI

  • Edge constraints force pragmatic mixes of symbolic and learned methods rather than ideological purity.
  • Hardware-centric design matters as much as data-centric approaches for deployed systems.
INSIGHT

Why Put Intelligence At The Edge

  • Edge AI places models on devices at the network edge to save bandwidth, latency, cost, reliability, and privacy (BLERP).
  • Running inference on-device distills sensor streams into compact signals for fast, private decisions.
INSIGHT

The Edge Device Spectrum

  • Edge devices span tiny microcontrollers to Linux-based systems and specialized AI accelerators.
  • Embedded ML is the most widely deployed deep learning today, e.g., always-on phone models across billions of handsets.
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