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Fast statistical timing analysis handling arbitrary delay correlations
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Source Annual ACM IEEE Design Automation Conference archive
Proceedings of the 41st annual Design Automation Conference table of contents
San Diego, CA, USA
SESSION: Statistical timing analysis table of contents
Pages: 337 - 342  
Year of Publication: 2004
ISBN:1-58113-828-8
Authors
Michael Orshansky  The University of Texas at Austin, Austin, TX
Arnab Bandyopadhyay  The University of Texas at Austin, Austin, TX
Sponsors
ACM: Association for Computing Machinery
SIGDA: ACM Special Interest Group on Design Automation
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 10,   Downloads (12 Months): 50,   Citation Count: 29
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ABSTRACT

An efficient statistical timing analysis algorithm that can handle arbitrary (spatial and structural) causes of delay correlation is described. The algorithm derives the entire cumulative distribution function of the circuit delay using a new mathematical formulation. Spatial as well as structural correlations between gate and wire delays can be taken into account. The algorithm can handle node delays described by non-Gaussian distributions. Because the analytical computation of an exact cumulative distribution function for a probabilistic graph with arbitrary distributions is infeasible, we find tight upper and lower bounds on the true cumulative distribution. An efficient algorithm to compute the bounds is based on a PERT-like single traversal of the sub-graph containing the set of N deterministically longest paths. The efficiency and accuracy of the algorithm is demonstrated on a set of ISCAS'85 benchmarks. Across all the benchmarks, the average rms error between the exact distribution and lower bound is 0.7%, and the average maximum error at 95th percentile is 0.6%. The computation of bounds for the largest benchmark takes 39 seconds.


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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CITED BY  30

Collaborative Colleagues:
Michael Orshansky: colleagues
Arnab Bandyopadhyay: colleagues