A Continuous Restricted Boltzmann Machine and Logistic Regression Framework for Circuit Classification

Abstract

Circuit identification and classification is an important field of research in Electronic Design Automation (EDA). This paper provides a novel framework for circuit classification based on a Continuous Restricted Boltzmann Machine and Logistic Regression. An undirected graph representation of a circuit CNF instance is created and employed to perform CNF-signatures’ search, thereof we classify it. A library with CNF-signatures of thousands of logic gates and functional blocks was pre-generated by our framework. These signatures are searched in the original CNF instance graph via traditional subgraph isomorphism algorithm and the results are applied as inputs for the Boltzmann Machine. Finally, a Logistic Regression classifier can determine to which class of the circuit each instance belongs. Our implementation is capable to correctly identify several circuit classes such as adders, multipliers and dividers with accuracy over 92%.