arXiv:2610.08876v1 Announce Type: new Abstract: Hardware faults can flip bits in the stored weights of a quantized neural network, potentially compromising its predictions. While such faults typically affect multiple parameters simultaneously, existing verifiers are limited to single-parameter perturbations due to the combinatorial explosion of possible flip locations in large networks.
Towards Verifying Neural Networks Against Multi-Parameter Bit-Flip Perturbations
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