In this paper we introduce and discuss the new concept of weighted don’t cares, i.e., we propose to enrich the notion of don’t cares, by assigning them a weight. These weights might be used to guide and refine the choices operated by the minimization algorithms in handling the don’t care conditions. We then propose, and experimentally validate, the first synthesis tool for functions with weighted don’t cares, called wBOOM. Experimental results show that wBOOM covers, on average, 66% more weighted don’t cares than the classical synthesis tool BOOM.
Weighted don't cares
BERNASCONI, ANNA;
2012-01-01
Abstract
In this paper we introduce and discuss the new concept of weighted don’t cares, i.e., we propose to enrich the notion of don’t cares, by assigning them a weight. These weights might be used to guide and refine the choices operated by the minimization algorithms in handling the don’t care conditions. We then propose, and experimentally validate, the first synthesis tool for functions with weighted don’t cares, called wBOOM. Experimental results show that wBOOM covers, on average, 66% more weighted don’t cares than the classical synthesis tool BOOM.File in questo prodotto:
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