Title
Non-parametric statistical static timing analysis: an SSTA framework for arbitrary distribution
Abstract
We present a new statistical STA framework based on Monte Carlo analysis that can deal with arbitrary statistical distribution and delay models. Order statistics (non-parametrics) is consistently adopted by which the timing analysis and criticality calculation become distribution-independent. To make Monte Carlo process computationally practical, delays are handled as vectors so that iterations are eliminated. The vector dimension or required number of Monte Carlo iterations which guarantees no timing violation at any user-specified probability is analytically determined. A path criticality metric using order statistics is also defined. Experimental results using various delay models show the validity and usefulness of our proposed algorithm.
Year
DOI
Venue
2008
10.1145/1391469.1391649
DAC
Keywords
Field
DocType
arbitrary distribution,criticality calculation,monte carlo iteration,order statistic,monte carlo analysis,timing analysis,delay model,monte carlo process computationally,non-parametric statistical static timing,path criticality,ssta framework,new statistical sta framework,arbitrary statistical distribution,monte carlo methods,kernel,static timing analysis,gaussian distribution,parametric statistics,monte carlo simulation,non parametric statistics,statistical distributions,order statistics,logic gates,monte carlo,probability,correlation,statistical distribution,algorithm design and analysis,iterative methods,statistical analysis
Monte Carlo method in statistical physics,Monte Carlo method,Mathematical optimization,Statistical static timing analysis,Markov chain Monte Carlo,Computer science,Hybrid Monte Carlo,Algorithm,Real-time computing,Parametric statistics,Static timing analysis,Monte Carlo molecular modeling
Conference
ISSN
Citations 
PageRank 
0738-100X
6
0.49
References 
Authors
9
4
Name
Order
Citations
PageRank
Masanori Imai180.91
Takashi Sato2131.63
Noriaki Nakayama3308.95
Kazuya Masu412036.37