Fine-Grained Fault Sensitivity Analysis of Vision Transformers Under Soft Errors

Over the past decade, deep neural networks (DNNs) have revolutionized the fields of computer vision (CV) and natural language processing (NLP), achieving unprecedented performance across a variety of tasks. The Vision Transformer (ViT) has emerged as a powerful alternative to convolutional neural networks (CNNs), leveraging self-attention mechanisms to capture long-range dependencies and global context. Owing to their flexible architecture and scalability, ViTs have been widely adopted in safety-critical applications such as autonomous driving, where system reliability is paramount. However, ViTs’ reliability issues induced by soft errors in large-scale digital integrated circuits have generally been overlooked. In this paper, we present a fine-grained fault sensitivity analysis of ViT variants under bit-flip fault injections, focusing on different ViT models, transformer encoder layers, weight matrix types, and attention-head dimensions. Experimental results demonstrate that the first transformer encoder layer is susceptible to soft errors due to its essential role in local and global feature extraction. Moreover, in the middle and later layers, the Multi-Layer Perceptron (MLP) sub-blocks dominate the computational workload and significantly influence representation learning, making them critical points of vulnerability. These insights highlight key reliability bottlenecks in ViT architectures when deployed in error-prone environments.