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ML Systems & EfficiencyVision Transformer2608.04035

CheckOne: Lightweight Fault Detection and Mitigation for Vision Transformers

Mohammad Hasan Ahmadilivani, Sven-Markus Loorits, Jaan Raik

cs.AR cs.AI

Abstract

The wide adoption of Vision Transformers (ViTs) in safety-critical applications raises reliability concerns related to hardware faults. Algorithm-Based Fault Tolerance (ABFT) methods have emerged as lightweight and symmetric protection mechanisms for DNNs. However, they are particularly challenging for ViTs due to their significant computational requirements. This work comprehensively evaluates the reliability of ViTs, emphasizing the need for symmetric protection in their layers. Furthermore, we present CheckOne, a novel, cost-effective method for fault detection and mitigation in ViTs that significantly reduces the computational cost compared to conventional ABFT. Through extensive experiments with multiple ViTs, CheckOne mitigates critical faults by up to $26\times$ and achieves an average 3.8x higher performance than ABFT in ViTs.

Topics

Classified with taxonomy v2 on Sat, 5 Sept 2026.

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