Neutron-Induced Error Rate of Vision Transformer Models on GPUs
Abstract
Vision Transformers (ViTs) are the new trend to improve performance and accuracy of machine learning. Through neutron beam experiments we show that ViTs have a higher FIT rate than traditional models but similar error criticality.
| Origin | Files produced by the author(s) |
|---|
