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Design space exploration using time and resource duality with the ant colony optimization
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Source Annual ACM IEEE Design Automation Conference archive
Proceedings of the 43rd annual Design Automation Conference table of contents
San Francisco, CA, USA
SESSION: Session 28: high-level exploration and optimization table of contents
Pages: 451 - 454  
Year of Publication: 2006
ISBN:1-59593-381-6
Authors
Gang Wang  University of California, Santa Barbara, CA
Wenrui Gong  University of California, Santa Barbara, CA
Brian DeRenzi  University of California, Santa Barbara, CA
Ryan Kastner  University of California, Santa Barbara, CA
Sponsors
SIGDA: ACM Special Interest Group on Design Automation
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 9,   Downloads (12 Months): 52,   Citation Count: 4
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ABSTRACT

Design space exploration during high level synthesis is often conducted through ad-hoc probing of the solution space using some scheduling algorithm. This is not only time consuming but also very dependent on designer's experience. We propose a novel design exploration method that exploits the duality between the time and resource constrained scheduling problems. Our exploration automatically constructs a high quality time/area tradeoff curve in a fast, effective manner. It uses the MAX-MIN ant colony optimization to solve both the time and resource constrained scheduling problems. We switch between the time and resource constrained algorithms to quickly traverse the design space. Compared to using force directed scheduling exhaustively at every time step, our algorithm provides a significant solution quality savings (average 17.3% reduction of resource counts) with similar run time on a comprehensive benchmark suite constructed with classic and real-life samples. Our algorithms scale well over different applications and problem sizes.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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M. Dorigo, V. Maniezzo, and A. Colorni. Ant System: Optimization by a Colony of Cooperating Agents. IEEE Transactions on Systems, Man and Cybernetics, Part-B, 26(1):29--41, February 1996.
 
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M. J. M. Heijligers, L. J. M. Cluitmans, and J. A. G. Jess. High-level synthesis scheduling and allocation using genetic algorithms. page 11, 1995.
 
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J. Madsen, J. Grode, P. V. Knudsen, M. E. Petersen, and A. Haxthausen. LYCOS: the Lyngby Co-Synthesis System. Design Automation for Embedded Systems, 2(2):125--63, March 1997.
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G. Wang, W. Gong, B. DeRenzi, and R. Kastner. Ant colony optimizations for resource and timing constrained instruction scheduling. IEEE Transaction on Computer-Aided Design, under review.
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Collaborative Colleagues:
Gang Wang: colleagues
Wenrui Gong: colleagues
Brian DeRenzi: colleagues
Ryan Kastner: colleagues