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Experimental analysis of verification parameters in a Constrained Random Verification (CRV) -based cross-level setup shows that different coverage metrics exhibit distinct saturation behaviors as verification parameters are varied.
This paper introduces a methodology, based on Negation-Permutation-Negation (NPN) classes, to explore a reduced number of approximate multipliers based on the error within DNN applications to overcome limitations of previous works.
A modular, end-to-end pipeline for FPV using Large Language Models is introduced and results show that the assertion pass rate can be improved by more than 2x and formal coverage can be improved by 86% with this pipeline compared to the baseline.
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