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Interval-based robust statistical techniques for non-negative convex functions, with application to timing analysis of computer chips
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Proceedings of the 2006 ACM symposium on Applied computing table of contents
Dijon, France
SESSION: Reliable computations and their applications (RCA) table of contents
Pages: 1645 - 1649  
Year of Publication: 2006
ISBN:1-59593-108-2
Authors
Michael Orshansky  University of Texas at Austin, Austin, TX
Wei-Shen Wang  University of Texas at Austin, Austin, TX
Martine Ceberio  University of Texas at El Paso, El Paso, TX
Gang Xiang  University of Texas at El Paso, El Paso, TX
Sponsor
SIGAPP: ACM Special Interest Group on Applied Computing
Publisher
ACM  New York, NY, USA
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ABSTRACT

In chip design, one of the main objectives is to decrease its clock cycle. On the design stage, this time is usually estimated by using worst-case (interval) techniques, in which we only use the bounds on the parameters that lead to delays. This analysis does not take into account that the probability of the worst-case values is usually very small; thus, the resulting estimates are over-conservative, leading to unnecessary over-design and under-performance of circuits. If we knew the exact probability distributions of the corresponding parameters, then we could use Monte-Carlo simulations (or the corresponding analytical techniques) to get the desired estimates. In practice, however, we only have partial information about the corresponding distributions, and we want to produce estimates that are valid for all distributions which are consistent with this information.In this paper, we develop a general technique that allows us, in particular, to provide such estimates for the clock time.


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:
Michael Orshansky: colleagues
Wei-Shen Wang: colleagues
Martine Ceberio: colleagues
Gang Xiang: colleagues