用最低有效位做校验，零内存开销保护ViT参数免受位翻转故障影响。
Zero Memory Overhead Approach for Protecting Vision Transformer Parameters
- 用参数最低有效位替代奇偶校验位，实现无额外存储开销的错误检测。
- 在检测到故障时将受影响参数置零，可使参数对位翻转的鲁棒性提升三个数量级。
- 适合自动驾驶等关键场景中对ViT模型可靠性的高要求应用。
视觉变换器（ViTs）因其自注意力机制，在图像分类、目标检测和分割等任务中表现优于卷积神经网络（CNNs）。随着ViTs在自动驾驶等安全关键应用中的普及，确保其参数在内存中正确运行变得至关重要，尤其需应对参数存储时的位翻转故障。本文提出一种零内存开销的容错技术，通过将参数最低有效位（LSB）替换为奇偶校验位，实现无需额外存储的错误检测。当检测到故障时，将受影响参数置零——因ViT模型多数参数接近零，此操作可有效防止精度下降。该方法显著提升各类ViT模型对位翻转的鲁棒性，最高达三个数量级，是一种适用于关键场景的高效零开销容错方案。
原文摘要 · Abstract (English)
Vision Transformers (ViTs) have demonstrated superior performance over Convolutional Neural Networks (CNNs) in various vision-related tasks such as classification, object detection, and segmentation due to their use of self-attention mechanisms. As ViTs become more popular in safety-critical applications like autonomous driving, ensuring their correct functionality becomes essential, especially in the presence of bit-flip faults in their parameters stored in memory. In this paper, a fault tolerance technique is introduced to protect ViT parameters against bit-flip faults with zero memory overhead. Since the least significant bits of parameters are not critical for model accuracy, replacing the LSB with a parity bit provides an error detection mechanism without imposing any overhead on the model. When faults are detected, affected parameters are masked by zeroing out, as most parameters in ViT models are near zero, effectively preventing accuracy degradation. This approach enhances reliability across ViT models, improving the robustness of parameters to bit-flips by up to three orders of magnitude, making it an effective zero-overhead solution for fault tolerance in critical applications.
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