DocumentCode
1786971
Title
Data mining in EDA - Basic principles, promises, and constraints
Author
Wang, L.-C. ; Abadir, M.S.
Author_Institution
Univ. of California at Santa Barbara, Santa Barbara, CA, USA
fYear
2014
fDate
1-5 June 2014
Firstpage
1
Lastpage
6
Abstract
This paper discusses the basic principles of applying data mining in Electronic Design Automation. It begins by introducing several important concepts in statistical learning and summarizes different types of learning algorithms. Then, the experience of developing a practical data mining application is described, including promises that are demonstrated through positive results based on industrial settings and constraints explained in their respective application contexts.
Keywords
data mining; electronic design automation; learning (artificial intelligence); EDA; data mining application; electronic design automation; industrial settings; statistical learning algorithm; Complexity theory; Computational modeling; Data mining; Data models; Kernel; Mathematical model; Support vector machines; Computer-Aided Design; Data Mining; Test; Verification;
fLanguage
English
Publisher
ieee
Conference_Titel
Design Automation Conference (DAC), 2014 51st ACM/EDAC/IEEE
Conference_Location
San Francisco, CA
Type
conf
DOI
10.1145/2593069.2596675
Filename
6881486
Link To Document