It’s no hallucination: Roughly 4 in 1,000 inferences inaccurate due to this alone
, hardware faults can corrupt data. No prizes for Meta there – phenomena such as"bit flips" that see data values changed from zero to one are well known and have even been attributed to cosmic rays hitting memory or hard disks.
"When this occurs during AI inference/servicing it can potentially lead to incorrect or degraded model output for users, ultimately affecting the quality and reliability of AI services," as Meta's boffins put it.their prevalence in its own infrastructure – but they are hard to detect at the best of times. In their latest paper, Meta's eggheads suggest the AI stack complicates matters further.
PVF is apparently"adaptable to different hardware fault models" and can be tweaked for different models and tasks. The paper explains that Meta simulated silent corruption incidents using"DLRM" – a tool the social media giant uses to generate personalized content recommendations. Under some circumstances, Meta's techies found four in every thousand inferences would be incorrect due to bit flips alone.
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