SENTRY: Statistical Reliability Analysis of Vision Transformers Under Soft Errors
With the growth of Vision Transformers in safety-critical domains like autonomous systems and medical imaging, ensuring their reliability against soft errors is paramount. While ViTs offer state-of-the-art accuracy, their massive parameter counts render exhaustive fault injection campaigns infeasible. To bridge this gap, a statistical fault injection framework is presented, leveraging finite-population sampling theory to provide formal reliability guarantees. It is demonstrated that failure rates are bounded within a 1% margin at 99\% confidence using only a few thousand samples, regardless of model scale. This methodology achieves up to a 10,700 times reduction in experimental cost compa...
| Authors | Pramit Kumar Bhaduri, Mahdi Taheri, Samira Nazari et al. |
|---|---|
| Primary category | cs.CV |
| Published | 30 May 2026 |
| Last updated at source | 30 May 2026 |
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