  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.
