In a U-Net for segmentation, bit-flip damage concentrates in batch-norm gamma and bias parameters, and rewriting risky floating-point exponents can reduce error rates with no runtime overhead.
Soft errors in advanced computer systems.IEEE design & test of computers, 22(3):258–266, 2005
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.CV 1years
2024 1verdicts
CONDITIONAL 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective
In a U-Net for segmentation, bit-flip damage concentrates in batch-norm gamma and bias parameters, and rewriting risky floating-point exponents can reduce error rates with no runtime overhead.