NPN classification is an essential problem in the design and verification of digital circuits. Most existing works explored variable symmetries and cofactor signatures to develop their classification methods. However, cofactor signatures only consider the face characteristics of Boolean functions. In this paper, we propose a new NPN classifier using both face and point characteristics of Boolean functions, including cofactor, influence, and sensitivity. The new method brings a new perspective to the classification of Boolean functions. The classifier only needs to compute some signatures, and the equality of corresponding signatures is a prerequisite for NPN equivalence. Therefore, these signatures can be directly used for NPN classification, thus avoiding the exhaustive transformation enumeration. The experiments show that the proposed NPN classifier gains better NPN classification accuracy with comparable speed.

翻译：NPN分类是数字电路设计与验证中的一个关键问题。现有研究大多利用变量对称性和余因子签名来发展其分类方法，但余因子签名仅考虑了布尔函数的面特征。本文提出一种新的NPN分类器，该分类器同时使用布尔函数的面特征与点特征，包括余因子、影响度和敏感度。这一新方法为布尔函数分类提供了全新视角。该分类器仅需计算若干签名，且对应签名的相等性是NPN等价性的前提条件，因此这些签名可直接用于NPN分类，从而避免穷举变换枚举。实验表明，所提NPN分类器在保持相当速度的同时，获得了更优的NPN分类准确率。