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A dynamic approach to controlling high-level synthesis CAD tools

In this paper, we present a novel model of CAD tool control that can be used in the constraint-directed control of high-level synthesis tools. To enable this control we introduce the concept of a design space reasoning mechanism. We formally describe a statistical based, machine learning process that automatically generates the tool control knowledge necessary to drive the design space reasoning mechanism. The representation of this tool control knowledge in the form of a fuzzy, linear differential, qualitative model is described. Finally, the experimental results obtained using the Magellan system are presented. >