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Confidential Computing within an AI Accelerator

on YouTube 20m
Talkat USENIX ATC '23 · 2023-07-10..07-12· [security]

speakers

name:Kapil Vaswani
links:none on file
name:Stavros Volos
linkedin:stavrosvolos
name:Cédric Fournet
links:none on file
name:Antonio Nino Diaz
links:none on file
name:Ken Gordon
links:none on file
name:Balaji Vembu
links:none on file
name:Sam Webster
links:none on file
name:David Chisnall
links:none on file
name:Saurabh Kulkarni
links:none on file
name:Graham Cunningham
links:none on file
name:Richard Osborne
links:none on file
name:Daniel Wilkinson
links:none on file

resources

video:https://youtu.be/nTVP6IvFlDY
slides:none on file

summary

Built an AI accelerator extension called IPU Trusted Extensions (ITX) that adds an on‑chip hardware root-of-trust and PCIe Gen4‑rate cryptographic engines, plus compiler/runtime changes to support multi‑party training without a CPU TEE. Implemented on Graphcore GC200 taped out at TSMC 7nm and tested on a dev board, it keeps models and data encrypted on the device, decrypts at chip boundary, and reports <5% overhead in typical DNN training and up to 17x speedups versus CPU SEV‑SNP solutions.

AI-written summary · about

chapters (8)

  1. 0:00Confidential computing in AI
  2. 1:07Why confidential computing for AI cloud
  3. 3:15Low-TCB AI accelerator design
  4. 8:49Trust extensions for IPU
  5. 13:44Remote attestation and key provisioning
  6. 16:35Replay and reorder attack protection
  7. 17:19Performance overheads and benchmarks
  8. 19:09Future of confidential AI

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