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On the futility of statistical power optimization
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Asia and South Pacific Design Automation Conference archive
Proceedings of the 2009 Asia and South Pacific Design Automation Conference table of contents
Yokohama, Japan
SESSION: Power analysis and optimization table of contents
Pages: 167-172  
Year of Publication: 2009
ISBN:978-1-4244-2748-2
Authors
Jason Cong  University of California at Los Angeles and California NanoSystems Institute
Puneet Gupta  University of California at Los Angeles
John Lee  University of California at Los Angeles
Sponsors
: IEEE Circuits and Systems Society
SIGDA: ACM Special Interest Group on Design Automation
IEICE ESS : Institute of Electronics, Information and Communication Engineers - Engineering Sciences Society
IPSJ SIGSLDM : Information Processing Society of Japan - SIG System LSI Design Methodology
Publisher
IEEE Press  Piscataway, NJ, USA
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ABSTRACT

In response to the increasing variations in integrated-circuit manufacturing, the current trend is to create designs that take these variations into account statistically. In this paper we try to quantify the difference between the statistical and deterministic optima of leakage power while making no assumptions about the delay model. We develop a framework for deriving a theoretical upper-bound on the suboptimality that is incurred by using the deterministic optimum as an approximation for the statistical optimum. On average, the bound is 2.4% for a suite of benchmark circuits in a 45nm technology. We further give an intuitive explanation and show, by using solution rank orders, that the practical suboptimality gap is much lower. Therefore, the need for statistical power modeling for the purpose of optimization is questionable.


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:
Jason Cong: colleagues
Puneet Gupta: colleagues
John Lee: colleagues