Accelerators and convergence measures for Monte-Carlo synthesis techniques

Author

Sridhar, Kamakshi

Author_Institution

Bell Labs., Lucent Technol., TX, USA

fYear

1996

fDate

11-14 Aug 1996

Firstpage

30

Lastpage

35

Abstract

Monte-Carlo synthesis techniques can be used to design new and complex systems that best meet a certain objective function with relative ease. Monte-Carlo synthesis is inefficient and does not provide obvious convergence measures. Accelerators based on probability distribution function shading and discriminant vector analysis are proposed. Convergence measures based on cluster identification and a statistical criterion are proposed. These enhancements are shown to significantly improve the performance of Monte-Carlo synthesis techniques. The implementation of these enhancements is shown through an example

Keywords

Monte Carlo methods; convergence of numerical methods; design engineering; probability; statistical analysis; Monte-Carlo synthesis techniques; accelerators; cluster identification; convergence measures; design theory; discriminant vector analysis; objective function; probability distribution function shading; statistical criterion; Convergence; Cost function; Design methodology; Feeds; Performance analysis; Probability distribution; Process design; Space exploration;

fLanguage

English

Publisher

ieee

Conference_Titel

Computers in Power Electronics, 1996., IEEE Workshop on

Conference_Location

Portland, OR

ISSN

1093-5142

Print_ISBN

0-7803-3977-0

Type

conf

DOI

10.1109/CIPE.1996.612333

Filename

612333