~/talk

Energy Proportional Edge Computing Infrastructure

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Proposes an Energy Proportional Edge Computing blueprint that uses time shifting and space shifting plus local power-state control to match compute to available low‑carbon energy. Shows a prototype that moves a video‑analytics workload from Xeon host to an IPU network card and reports ~30% energy savings on real runs, and modeled savings of ~$70M/yr and ~140M kg CO2 for 42,000 sites. Uses intent‑driven orchestration, federated telemetry and learning, and meets 100 ms SLOs with 20–30 ms reaction.

AI-written summary · about

  1. 0:00Energy management at scale
  2. 1:05Edge computing deployment models
  3. 3:37Sustainability challenges
  4. 5:02Energy management pillars
  5. 8:15Hardware and software efficiency
  6. 10:03Energy proportional edge workloads
  7. 14:00Adaptive orchestration
  8. 16:27Proportional systems modeling
  9. 22:31Prototype savings
  10. 25:40Observability and federated learning
  11. 28:30Energy time and space shifting
  12. 32:41Intent-driven power savings
  13. 37:36Federated Nearby Computing
  14. 39:01Standardization needs
  15. 40:49AI policies and emissions
  16. 41:52Attestation and transparency
  17. 42:35Q&A
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