A framework for low complexitgy static learning

Authors:
Emil Gizdarski;Hideo Fujiwara
Affiliations:
Nara Institute of Science and Technology and Department of Computer Systems, University of Rousse, 7017 Rousse, Bulgaria;Nara Institute of Science and Technology, Ikoma, Nara 630-0101, Japan
Venue:
Proceedings of the 38th annual Design Automation Conference
Year:
2001

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Abstract

In this paper, we present a new data structure for a complete implication graph and two techniques for low complexity static learning. We show that using static indirect &Lgr-implications and super gate extraction some hard-to-detect static and dynamic indirect implications are easily derived during static and dynamic learning as well as branch and bound search. Experimental results demonstrated the effectiveness of the proposed data structure and learning techniques.