ALBERTA: ALgorithm-Based Error Resilience in Transformer Architectures. (arXiv:2310.03841v1 [cs.CR])

Vision Transformers are being increasingly deployed in safety-critical
applications that demand high reliability. It is crucial to ensure the
correctness of their execution in spite of potential errors such as transient
hardware errors. We propose a novel algorithm-based resilience framework called
ALBERTA that allows us to perform end-to-end resilience analysis and protection
of transformer-based architectures. First, our work develops an efficient
process of computing and ranking the resilience of transformers layers. We find
that due to the large size of …

alberta algorithm analysis applications called correctness critical demand end end-to-end error errors framework hardware high novel reliability resilience safety safety-critical transformers