NDLI: Supervised design space exploration by compositional approximation of Pareto sets

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Supervised design space exploration by compositional approximation of Pareto sets

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Supervised design space exploration by compositional approximation of Pareto sets

Content Provider	ACM Digital Library
Author	Liu, Hung-Yi Carloni, Luca Petracca, Michele Diakonikolas, Ilias
Abstract	Technology scaling allows the integration of billions of transistors on the same die but CAD tools struggle in keeping up with the increasing design complexity. Design productivity for multi-core SoCs increasingly depends on creating and maintaining reusable components and hierarchically combining them to form larger composite cores. Characterizing such composite cores with respect to their power/performance tradeoffs is critical for design reuse across various products and relies heavily on synthesis tools. We present CAPS, an online adaptive algorithm that efficiently explores the design space of any given core and returns an accurate characterization of its implementation tradeoffs in terms of an approximate Pareto set. It does so by supervising the order of the time-consuming logic-synthesis runs on the core's components. Our algorithm can provably achieve the desired precision on the approximation in the shortest possible time, without having any a-priori information on any component. We also show that, in practice, CAPS works even better than what is guaranteed by the theory.
Starting Page	399
Ending Page	404
Page Count	6
File Format	PDF
ISBN	9781450306362
ISSN	85644924
DOI	10.1145/2024724.2024818
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-06-05
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Design reuse System-on-chip System-level design
Content Type	Text
Resource Type	Article

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