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Incorporating logic exclusivity (LE) constraints in noise analysis using gain guided backtracking method
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International Conference on Computer Aided Design archive
Proceedings of the 2008 IEEE/ACM International Conference on Computer-Aided Design table of contents
San Jose, California
SESSION: Exploiting logic constraints for noise analysis table of contents
Pages 783-789  
Year of Publication: 2008
ISBN ~ ISSN:1092-3152 , 978-1-4244-2820-5
Authors
Ruiming Li  Sun Microsystems Inc.
An-Jui Shey  Sun Microsystems Inc.
Michel Laudes  Sun Microsystems Inc.
Sponsors
: IEEE CASS/CANDE
: IEEE Council on Electronic Design Automation (CEDA)
SIGDA: ACM Special Interest Group on Design Automation
Publisher
IEEE Press  Piscataway, NJ, USA
Bibliometrics
Downloads (6 Weeks): 6,   Downloads (12 Months): 17,   Citation Count: 0
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ABSTRACT

Crosstalk noise becomes one of the critical issues gating design closure for nano-meter designs. Pessimism in noise analysis can lead to significant additional time spent addressing false violations. Taking logic correlation into consideration, noise analysis can reduce pessimism significantly by eliminating false noise signals [1]-[3][5]-[7][10]-[13]. Eliminating the aggressors from the aggressor candidate set that can not switch simultaneously restricted by the logic exclusivity (LE) relationship among them can save simulation time as well. The LE problem, being proved as NP-complete, is basically to determine the subset (possibly multiple equivalent subsets) of a given aggressor candidate set which has the largest combined weight out of all possible subsets governed by logic exclusivity constraints. This paper presents a new approach in resolving the LE problem, which employs a gain guided backtrack search technique that does not require exhaustive search of all the binary paths to reach an optimal solution. We first prove that under certain conditions, if the gain at each level is non-negative, then the result will be optimal. Based on this theorem, a new algorithm is developed. The experimental results demonstrate the efficiency and accuracy of this approach. The algorithm can quickly find the optimal solutions for most cases from industry designs and outperforms other methods.


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. Palla, J. Bargfrede, K. Koch, W. Anheier, and R. Drechsler. False noise analysis using branch & bound and sat. In TAU Proceedings, pages 32--37, 2008.
 
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D. Sinha, S. Abbaspour, G, and Schaeffer. Constrained aggressor set selection for maximum coupling noise. In TAU Proceedings, pages 38--43, 2008.
 
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K. Tseng and M. Horowitz. False coupling exploration in timing analysis. Tran. CAD, 24(11):1795--1805, November 2005.
 
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Collaborative Colleagues:
Ruiming Li: colleagues
An-Jui Shey: colleagues
Michel Laudes: colleagues