A framework for low complexity static learning

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 /spl and/-implications and super gate extraction some hard-to-detect static and dynamic indirect implications are easily derived d...

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Main Authors: Gizdarski, E., Fujiwara, H.
Format: Conference Proceeding
Language:English
Subjects:
Automatic test pattern generation
Data mining
Data structures
Decision trees
Electronic design automation and methodology
Logic testing
NP-complete problem
Permission
System testing
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Fujiwara, H.
description 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 /spl and/-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.
doi_str_mv 10.1109/DAC.2001.156199
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source IEEE Electronic Library (IEL) Conference Proceedings
subjects Automatic test pattern generation
Data mining
Data structures
Decision trees
Electronic design automation and methodology
Logic testing
NP-complete problem
Permission
System testing
title A framework for low complexity static learning
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