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    block this user An-Ping Li

    Research Fellow

    Beijing 100085, P.R.China

    Support Vector Machines for Analog Circuit Performance Representation

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    The use of Support Vector Machines (SVMs) to represent the performance space of analog circuits is explored. In abstract terms, an analog circuit maps a set of input design parameters to a set of performance figures. This function is usually evaluated through simulations and its range defines the feasible performance space of the circuit. In this paper, we directly model performance spaces as mathematical relations. We study approximation approaches based on two-class and one-class SVMs, the latter providing a better tradeoff between accuracy and complexity avoiding "curse of dimensionality" issues with 2-class SVMs. We propose two improvements of the basic one-class SVM performances: conformal mapping and active learning. Finally we develop an efficient algorithm to compute projections, so that topdown methodologies can be easily supported.

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    Description

    Title : Support Vector Machines for Analog Circuit Performance Representation
    Abstract : The use of Support Vector Machines (SVMs) to represent the performance space of analog circuits is explored. In abstract terms, an analog circuit maps a set of input design parameters to a set of performance figures. This function is usually evaluated through simulations and its range defines the feasible performance space of the circuit. In this paper, we directly model performance spaces as mathematical relations. We study approximation approaches based on two-class and one-class SVMs, the latter providing a better tradeoff between accuracy and complexity avoiding "curse of dimensionality" issues with 2-class SVMs. We propose two improvements of the basic one-class SVM performances: conformal mapping and active learning. Finally we develop an efficient algorithm to compute projections, so that topdown methodologies can be easily supported.
    Subject : unspecified
    Area : Mathematics
    Language : English
    Affiliations
    Url : http://www-cad.eecs.berkeley.edu/Respep/Research/asves/paper2003/Debernardinis_dac03.pdf
    Doi : 10.1.1.14.9374

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    An-Ping's Peer Evaluation activity

    Emailed by 1
    • Anonymous : 1
    Downloads 662
    Views 527
    Full text requests 9
    Followed by 2

    An-Ping has...

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