Quasi-exact logic functions through classification trees

In this paper we propose a novel ML-driven synthesis methodology that allows to describe generic Boolean functions through a representative subset of core expressions using Classification Trees (CTs). Obtained circuits are able to mimic Boolean functions to a certain degree of accuracy, hence the name quasi-exact logic functions. The proposed synthesis flow enables a smart hardware mapping of quasi-exact logic functions by means of reduced and ordered decision diagrams. Experiments conducted on a subset of open-source benchmarks demonstrate that CTs are indeed able to cover rather complex Boolean functions with a very high degree of accuracy, 88% on average, still requiring 3× less area over standard multi-level circuit counterparts.