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Algorithms for simultaneous satisfaction of multiple constraints and objective optimization in a placement flow with application to congestion control
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Source Annual ACM IEEE Design Automation Conference archive
Proceedings of the 39th annual Design Automation Conference table of contents
New Orleans, Louisiana, USA
SESSION: Floorplanning and placement table of contents
Pages: 854 - 859  
Year of Publication: 2002
ISBN ~ ISSN:0738-100X , 1-58113-461-4
Authors
Ke Zhong  University of Illinois at Chicago, Chicago, IL
Shantanu Dutt  University of Illinois at Chicago, Chicago, IL
Sponsor
SIGDA: ACM Special Interest Group on Design Automation
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 4,   Downloads (12 Months): 14,   Citation Count: 2
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ABSTRACT

This paper addresses the problem of tackling multiple constraints simultaneously during a partitioning driven placement (PDP) process, where a larger solution space is available for constraint-satisfying optimization compared to post placement methods. A general methodology of multi-constraint satisfaction that balances violation correction and primary optimization is presented. A number of techniques are introduced to ensure its convergence and enhance its solution search capability with intermediate relaxation. Application of our approach to congestion control modeled as pin density and external net distribution balance constraints shows it effectively reduces overall congestion by 14.3% and improves chip area by 8.9%, with reasonable running time and only 1.6% increase in wire length. As far as we know, this is the first time an approach to congestion reduction during placement optimization produced good congestion improvement with very small wire length increase.


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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