As transformer-based object detection models progress, their impact in critical sectors like autonomous vehicles and aviation is expected to grow. Soft errors causing bit flips during inference have significantly impacted DNN performance, altering predictions. Traditional range restriction solutions for CNNs fall short for transformers. This study introduces the Global Clipper and Global Hybrid Clipper, effective mitigation strategies specifically designed for transformer-based models. It significantly enhances their resilience to soft errors and reduces faulty inferences to ~ 0\%. We also detail extensive testing across over 64 scenarios involving two transformer models (DINO-DETR and Lite-DETR) and two CNN models (YOLOv3 and SSD) using three datasets, totalling approximately 3.3 million inferences, to assess model robustness comprehensively. Moreover, the paper explores unique aspects of attention blocks in transformers and their operational differences from CNNs.

翻译：随着基于Transformer的目标检测模型的发展，其在自动驾驶和航空等关键领域的影响预计将日益扩大。推理过程中导致位翻转的软错误已显著影响深度神经网络的性能，改变预测结果。针对卷积神经网络的传统范围限制解决方案对Transformer模型效果有限。本研究提出了全局裁剪器与全局混合裁剪器，这是专门为基于Transformer的模型设计的有效缓解策略。该策略显著增强了模型对软错误的鲁棒性，并将错误推理降低至约0%。我们还详细介绍了在超过64种场景下进行的广泛测试，涉及两种Transformer模型（DINO-DETR和Lite-DETR）与两种CNN模型（YOLOv3和SSD），使用三个数据集，总计约330万次推理，以全面评估模型鲁棒性。此外，本文探讨了Transformer中注意力模块的独特性质及其与CNN在操作上的差异。