Rethinking NPN Classification from Face and Point Characteristics of Boolean Functions

TL;DR

This paper introduces a novel NPN classification method for Boolean functions that combines face and point characteristics, improving accuracy and efficiency by avoiding exhaustive enumeration.

Contribution

It proposes a new classifier using combined face and point signatures, enhancing NPN classification without exhaustive transformations.

Findings

Achieves higher classification accuracy

Maintains comparable speed to existing methods

Utilizes combined signatures for efficient NPN equivalence detection

Abstract

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.