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Maximal Causality Analysis

Authors: Klaus Schneider 0001; Jens Brandt 0001; Tobias Schüle; Thomas Tuerk;

Maximal Causality Analysis

Abstract

Perfectly synchronous systems immediately react to the inputs of their environment, which may lead to so-called causality cycles between actions and their trigger conditions. Algorithms to analyze the consistency of such cycles usually extend data types by an additional value to explicitly indicate unknown values. In particular, Boolean functions are thereby extended to ternary functions. However, a Boolean function usually has several ternary extensions, and the result of the causality analysis depends on the chosen ternary extension. In this paper, we show that there always is a maximal ternary extension that allows one to solve as many causality problems as possible. Moreover, we elaborate the relationship to hazard elimination in hardware circuits, and finally show how the maximal ternary extension of a Boolean function can be efficiently computed by means of binary decision diagrams.

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    influence
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Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
21
Average
Top 10%
Top 10%
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