Title :
An active learning scheme using support vector machines for analog circuit feasibility classification
Author :
Ding, Mengmeng ; Vemur, R.I.
Author_Institution :
Cincinnati Univ., OH, USA
Abstract :
This paper presents a new active learning scheme using support vector machines (SVMs) and its application in identifying the feasibility design space of analog circuit. The proposed methodology uses a committee of SVM classifiers to exclude a large portion of the entire design space and samples only the feasibility region and its neighboring. We also introduce three accuracy metrics due to the extreme sparsity of the feasibility design space in the entire design space. Experimental results show that the three accuracy metrics of the final constructed classifier are much better than those of a classifier constructed by a passive learning scheme which samples the entire design space uniform randomly.
Keywords :
analogue circuits; circuit CAD; integrated circuit design; integrated circuit modelling; learning (artificial intelligence); support vector machines; accuracy metrics; active learning scheme; analog circuit feasibility classification; feasibility design space; passive learning scheme; support vector machines; Analog circuits; Circuit synthesis; Circuit topology; Force sensors; Machine learning; Mathematical model; Size control; Support vector machine classification; Support vector machines; Upper bound;
Conference_Titel :
VLSI Design, 2005. 18th International Conference on
Print_ISBN :
0-7695-2264-5
DOI :
10.1109/ICVD.2005.47