Vision transformers (ViTs) that leverage self-attention mechanism have shown superior
performance on many classical vision tasks compared to convolutional neural networks
(CNNs) and gain increasing popularity recently. Existing ViTs' works mainly optimize
performance and accuracy, but ViTs' reliability issues induced by soft errors in large-scale
VLSI designs have generally been overlooked. In this work, we mainly study the reliability of
ViTs and investigate the vulnerability from different architecture granularities ranging from …

Vision Transformers (ViTs) that leverage self-attention mechanism have shown superior
performance on many classical vision tasks compared to convolutional neural networks
(CNNs) and gain increasing popularity recently. Existing ViTs works mainly optimize
performance and accuracy, but ViTs reliability issues induced by soft errors in large-scale
VLSI designs have generally been overlooked. In this work, we mainly study the reliability of
ViTs and investigate the vulnerability from different architecture granularities ranging from …