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TreeFI: Value-Aware Statistical Fault Injection for Deep Neural Networks
Reliability evaluation of deep neural networks under hardware faults commonly relies on fault injection, but exhaustive campaigns are intractable for modern models and datasets. Statistical fault injection reduces this cost, yet existing approaches still require large injection budgets because they do not explicitly exploit a key property of floating-point faults: the effect of a bit flip depends strongly on the value being corrupted. We propose TreeFI, a value-aware statistical fault-injection methodology for FP32 single-bit faults in DNN activations and weights. TreeFI partitions each layer'
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-04T09:15:06.000Z
First collected: 2026-09-20T22:31:48.298Z. This is not the publication date.