# Fine-Grained Fault Sensitivity Analysis of Vision Transformers Under Soft Errors
## 3 Results  
### Experimental Setup  
We conducted fault simulation experiments on a GPU server equipped with an Intel Xeon Gold 6253 CL CPU, 500 GB of RAM, and two NVIDIA Tesla V100 GPUs (totaling 64 GB of global memory), using CUDA 12.4, PyTorch 2.5.1, and Python 3.7. Seven different ViT models from the HuggingFace timm library (v1.0.12) were evaluated by performing inference on randomly selected image batches from the ImageNet dataset. All models belonged to the Original ViT with different blocks and input patches. Table 3 shows the essential features of the evaluated models, including their model names, the number of input patches, the number of layers, accuracy, and parameter size. In our experiments, we adopted two fault injection methodologies to enable multi-granularity analyses. The first approach applied a range of bit error rates (BERs) on a logarithmic scale for model-wise evaluation, while the second method employed fixed BER values for type-wise, layer-wise, and head-wise analysis. The BER denotes the fraction of total integer weight bits that are randomly selected and flipped. To evaluate the model's robustness under different fault tolerance requirements, we defined the 0%, 1%, 5%, and 10% accuracy loss BER thresholds as the BER at which the model's classification error increased by no more than 0, 1, 5, and 10 percentage points, respectively, compared to its reference performance. These thresholds reflect varying levels of robustness: A higher 0% accuracy loss threshold means that the model can tolerate more errors without loss of accuracy, while the 1% accuracy loss threshold indicates near-error-free resilience suitable for safety-critical scenarios; the 5% threshold represents moderate degradation acceptable in non-critical applications; and the 10% threshold cor  
Figure 3: Fault resilience evaluation platform for ViT models. Pink circles on the left denote neurons operating without fault injection, while yellow circles on the right represent neurons injected with faults. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model Name * & **Input Size** & **Patches** & **Layers** & **Accuracy (\%)** & **Size (MB)** \\ \hline B16-224-augreg & 224 \(\times\) 224 & 16 & 12 & 84.536\% & 330 \\ B16-384-augreg & 384 \(\times\) 384 & 16 & 12 & 85.998\% & 330 \\ B32-224-augreg & 224 \(\times\) 224 & 32 & 12 & 80.718\% & 336 \\ B32-384-augreg & 384 \(\times\) 384 & 32 & 12 & 83.350\% & 336 \\ L16-384-augreg & 384 \(\times\) 384 & 16 & 24 & 87.098\% & 1160 \\ L32-384-orig & 384 \(\times\) 384 & 32 & 24 & 81.512\% & 1169 \\ H14-224-orig ** & 224 \(\times\) 224 & 14 & 32 & 88.272\% & 2410 \\ \hline \hline \end{tabular}  
* We use brief notation to indicate the model size, input patch size, and input image size: for instance, B16-384 means the “Base” variant with 16 \(\times\) 16 input patch size and 384 \(\times\) 384 input image size. ** “orig” means trained with normal methods. “augreg” means trained with additional augmentation and regularization.  
\end{table} 
Table 3: Summary of ViT variants: names, patch sizes, block counts, accuracy, and size of parameters. accuracy loss, marking a limit beyond which the model's predictions become unreliable without additional protection mechanisms. A higher BER threshold means that the model can tolerate more errors without loss of accuracy. To quantify the reliability of different components within ViT models, we introduced the vulnerability factor as a metric, defined as the accuracy degradation between the fault-injected model and its counterpart. To ensure the statistical robustness of the results, each experiment was performed through ten independent injection trials, each initialized with a different random seed. The number of iterations was selected to balance computational cost and result stability and enabled the reporting of mean, standard deviation, and worst-case accuracy under fault conditions. The error bars for all the experimental results were calculated based on 95% confidence intervals.