arXiv:2606.07620v1 Announce Type: cross Abstract: 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\% confidenc
As AI models, particularly Vision Transformers, are deployed in safety-critical applications, the urgency to ensure their reliability against physical errors due to hardware degradation or radiation is growing.
Ensuring the statistical reliability of Vision Transformers is critical for the safe and trustworthy adoption of AI in autonomous systems and medical imaging, directly impacting regulatory approval and public trust.
This research provides a more efficient and formally guaranteed method for reliability analysis, moving beyond infeasible brute-force fault injection for large models.
- · AI hardware manufacturers
- · Autonomous vehicle developers
- · Medical AI companies
- · AI certification bodies
- · Companies with unreliable AI systems
- · Traditional exhaustive testing methodologies
Increased confidence in the deployment of Vision Transformers in high-stakes environments.
Faster development and regulatory approval timelines for AI-powered autonomous and medical solutions.
Heightened public acceptance and rapid integration of AI into daily critical infrastructure.
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Read at arXiv cs.LG