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Statistical crosstalk aggressor alignment aware interconnect delay calculation
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Source International Workshop on System-Level Interconnect Prediction archive
Proceedings of the 2006 international workshop on System-level interconnect prediction table of contents
Munich, Germany
SESSION: Physical interconnect analysis and optimization table of contents
Pages: 91 - 97  
Year of Publication: 2006
ISBN:1-59593-255-0
Authors
Andrew B. Kahng  University of California at San Diego, La Jolla, CA
Bao Liu  University of California at San Diego, La Jolla, CA
Xu Xu  University of California at San Diego, La Jolla, CA
Sponsors
ACM: Association for Computing Machinery
SIGDA: ACM Special Interest Group on Design Automation
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 2,   Downloads (12 Months): 21,   Citation Count: 2
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ABSTRACT

Crosstalk aggressor alignment induces significant interconnect delay variation and needs to be taken into account in a statistical timer. In this paper, we approximate crosstalk aggressor alignment induced interconnect delay variation in a piecewise-quadratic function, and present closed form formulas for statistical interconnect delay calculation with crosstalk aggressor alignment variation. Our proposed method can be easily integrated in a statistical timer, where traditional corner-based timing windows are replaced by probabilistic distributions of crosstalk aggressor alignment, which can be refined by similar delay calculation iterations. Runtime is O(N) for initial delay calculation of N sampling crosstalk aggressor alignments, while pdf propagation and delay updating requires constant time. We compare with SPICE Monte Carlo simulations on Berkeley predictive model 70nm global interconnect structures and 130nm industry design instances. Our experimental results show that crosstalk aggressor alignment oblivious statistical delay calculation could lead to up to 114.65% (71.26%) mismatch of interconnect delay means (standard deviations), while our method gives output signal arrival time means (standard deviations) within 2.09% (3.38%) of SPICE Monte Carlo simulation results.


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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Collaborative Colleagues:
Andrew B. Kahng: colleagues
Bao Liu: colleagues
Xu Xu: colleagues