New multivalued functional decomposition algorithms based on MDDs

Authors:
C. M. Files;M. A. Perkowski
Affiliations:
Dept. of Electr. Eng., Portland State Univ., OR;-
Venue:
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Year:
2006

Citing 0
Cited 4

Multi-level logic optimization

Logic Synthesis and Verification
Multiple-valued logic synthesis and optimization

Logic Synthesis and Verification
Learning Hardware Using Multiple-Valued Logic, Part 1: Introduction and Approach

IEEE Micro
Boolean factoring and decomposition of logic networks

Proceedings of the 2008 IEEE/ACM International Conference on Computer-Aided Design

Quantified Score

Hi-index	0.03

Visualization

Abstract

This paper presents two new functional decomposition partitioning algorithms that use multivalued decision diagrams (MDDs). MDDs are an exceptionally good representation for generalized decomposition because they are canonical and they can represent very large functions. Algorithms developed in this paper are for Boolean/multivalued input and output, completely/incompletely specified functions with application to logic synthesis, machine learning, data mining and knowledge discovery in databases. We compare the run-times and decision diagram sizes of our algorithms to existing decomposition partitioning algorithms based on decision diagrams. The comparisons show that our algorithms are faster and do not result in exponential diagram sizes when decomposing functions with small bound sets