发表机构
Tallinn University of Technology（塔林理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对视觉Transformer的硬件故障问题，提出轻量级故障检测与缓解方法CheckOne，其缓解关键故障能力达26倍，平均性能较传统ABFT高3.8倍，解决了ViTs应用中的可靠性与计算成本矛盾。
AI 中文摘要
视觉Transformer（ViTs）在安全关键型应用中的广泛应用引发了与硬件故障相关的可靠性问题。基于算法的故障容错（ABFT）方法已成为深度神经网络（DNNs）的轻量级对称保护机制，但由于ViTs的计算需求显著，这些方法对ViTs而言极具挑战性。本研究全面评估了ViTs的可靠性，强调其各层需要对称保护；此外，我们提出了CheckOne，这是一种用于ViTs故障检测与缓解的新型高性价比方法，与传统ABFT相比大幅降低了计算成本。通过对多种ViTs的大量实验，CheckOne可缓解高达26倍的关键故障，且在ViTs上的平均性能比ABFT高3.8倍。
英文摘要
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.
CommentsAccepted at IEEE DFTS'26, 4 pages, 3 figures, 2 tables
