~/talk

SAVE: Software-Implemented Fault Tolerance for Model Inference against GPU Memory..

Talkat USENIX ATC '25 · 2025-07-07..07-09· [machine-learning, security]
links:none on file
name:Bin Xu
links:none on file
name:Jinyu Gu
links:none on file
slides:none on file

Claims software-only SAVE keeps model accuracy even under 4,000 GPU memory bit flips while adding less than 9% runtime overhead and under 1% correctness impact. It identifies vulnerable bits with static range and bit‑attribution analysis, places high‑risk tensor blocks into tiny reliable SRAM, and offloads checks to the CPU with asynchronous CPU verification plus a lightweight in‑kernel range check and a mixed‑precision CPU recompute for recovery. Tested against real bit‑flip traces and on RTX 1490, it restarts from the affected layer to recover.

AI-written summary · about

  1. 0:00SAVE introduction
  2. 2:09Existing solutions for bit flips
  3. 3:53SAVE’s key insights
  4. 6:00SAVE workflow stages
  5. 9:15Allocation and verification
  6. 10:42Error handling and latency
  7. 12:09Performance overhead results
  8. 13:59Conclusion and Q&A
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