Efficient NPN Classification for Large Functions using Graph Neural Network

arXiv cs.AINovember 3, 2026en

arXiv:2611.00339v1 Announce Type: new Abstract: Two Boolean functions are considered NPN equivalent if one can be transformed into the other by negating inputs, permuting inputs, or negating the output. Efficient NPN classification is valuable in fields such as logic synthesis, quantum computing, and information theory. However, exact NPN classification demands exhaustive enumeration of all possible input permutations and negations. Its complexity grows exponentially, making it impractical for larger functions with more inputs. In this paper, we introduce the first learning-based formulation for NPN classification by recasting it as NPN action prediction. Our method represents Boolean functi

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