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Using estimates from behavioral synthesis tools in compiler-directed design space exploration

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Published:02 June 2003Publication History

ABSTRACT

This paper considers the role of performance and area estimates from behavioral synthesis in design space exploration. We have developed a compilation system that automatically maps high-level algorithms written in C to application-specific designs for Field Programmable Gate Arrays (FPGAs), through a collaboration between parallelizing compiler technology and high-level synthesis tools. Using several code transformations, the compiler optimizes a design to increase parallelism and utilization of external memory bandwidth, and selects the best design among a set of candidates. Performance and area estimates from behavioral synthesis provide feedback to the compiler to guide this selection. Estimates can be derived far more quickly (up to several orders of magnitude faster) than full synthesis and place-and-route, thus allowing the compiler to consider many more designs than would otherwise be practical. In this paper, we examine the accuracy of the estimates from behavioral synthesis as compared to the fully synthesized designs for a collection of 209 designs for five multimedia kernels. Though the estimates are not completely accurate, our results show that the same design would be selected by the design space exploration algorithm, whether we use estimates or actual results from place-and-route, because it favors smaller designs and only increases complexity when the benefit is significant.

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  1. Using estimates from behavioral synthesis tools in compiler-directed design space exploration

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          cover image ACM Conferences
          DAC '03: Proceedings of the 40th annual Design Automation Conference
          June 2003
          1014 pages
          ISBN:1581136889
          DOI:10.1145/775832

          Copyright © 2003 ACM

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          Publication History

          • Published: 2 June 2003

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          DAC '03 Paper Acceptance Rate152of628submissions,24%Overall Acceptance Rate1,770of5,499submissions,32%

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