New Statistical Framework Enables Efficient Reliability Testing of Vision Transformers Against Hardware Errors
Researchers have introduced SENTRY, a statistical fault injection framework designed to assess how Vision Transformers (ViTs) behave when hardware soft errors — random bit-flips — occur during inference. Because ViTs contain millions of parameters, exhaustive fault testing is computationally prohibitive, and SENTRY uses finite-population sampling theory to provide formal reliability guarantees with only a few thousand test samples. The findings matter for safety-critical deployments such as autonomous vehicles and medical imaging, where undetected hardware faults could cause catastrophic model failures.
SENTRY is a statistical fault injection framework presented in a preprint on arXiv that addresses the reliability of Vision Transformers (ViTs) under soft errors — transient hardware faults that flip individual bits in memory or computation. The core contribution is the application of finite-population sampling theory, which allows the framework to bound failure rates within a 1% margin at 99% confidence using only a few thousand fault injection samples, regardless of model size. This represents up to a 10,700-fold reduction in experimental cost compared to exhaustive fault injection campaigns. Evaluations on ViT-Tiny and ViT-Small architectures revealed a highly non-uniform reliability landscape: while only about 3% of FP32 bit-flips cause failures, nearly all such failures result in catastrophic accuracy collapse rather than graceful degradation. Vulnerabilities were specifically localized to normalization layers and to the exponent bits of the IEEE-754 floating-point format, which are disproportionately influential on model outputs. The authors argue these findings provide both a mathematical foundation and actionable guidance for designing hardened ViT architectures suitable for edge deployment in safety-critical systems.
What's missing
The study evaluates only two relatively small ViT variants (ViT-Tiny and ViT-Small); it is unclear whether the reliability landscape and vulnerability localizations generalize to larger or more architecturally diverse models (e.g., ViT-Large, Swin Transformers). The framework assumes FP32 precision, leaving open questions about behavior under quantized or mixed-precision deployments common in edge hardware. Additionally, the work has not yet undergone peer review, as it is a preprint.
What different sources said
- arXiv cs.AICenter
SENTRY: Statistical Reliability Analysis of Vision Transformers Under Soft Errors
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