Behavioral-level performance modeling of analog and mixed-signal systems using support vector machines
Behavioral-level performance modeling of analog and mixed-signal systems using support vector machines
This paper presents a novel behavioral-level analog and mixed- signal (AMS) system performance modeling methodology using support vector machines (SVM). The method relies on linearly graded sub-spaces to model complex multi-dimensional performance spaces. A detailed evaluation of the method has been carried out for the purpose of potential use for AMS synthesis. The method has been applied to a complex non- ideal 2nd order Sigma-Delta modulator (SDM) and results show good accuracy of performance modeling and numerical efficiency.
28-33
Ren, Xianqiang
c7f83e76-c48c-4690-86e3-f5fddb5e2198
Kazmierski, Tom
a97d7958-40c3-413f-924d-84545216092a
2006
Ren, Xianqiang
c7f83e76-c48c-4690-86e3-f5fddb5e2198
Kazmierski, Tom
a97d7958-40c3-413f-924d-84545216092a
Ren, Xianqiang and Kazmierski, Tom
(2006)
Behavioral-level performance modeling of analog and mixed-signal systems using support vector machines.
Behavioral modeling and simulation conference, San Jose, California, United States.
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Abstract
This paper presents a novel behavioral-level analog and mixed- signal (AMS) system performance modeling methodology using support vector machines (SVM). The method relies on linearly graded sub-spaces to model complex multi-dimensional performance spaces. A detailed evaluation of the method has been carried out for the purpose of potential use for AMS synthesis. The method has been applied to a complex non- ideal 2nd order Sigma-Delta modulator (SDM) and results show good accuracy of performance modeling and numerical efficiency.
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Published date: 2006
Additional Information:
Event Dates: 2006.09
Venue - Dates:
Behavioral modeling and simulation conference, San Jose, California, United States, 2006-08-31
Organisations:
EEE
Identifiers
Local EPrints ID: 264619
URI: http://eprints.soton.ac.uk/id/eprint/264619
PURE UUID: 33cff924-7656-48f0-b54d-d71cf9efc9e2
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Date deposited: 03 Oct 2007
Last modified: 10 Dec 2021 21:48
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Contributors
Author:
Xianqiang Ren
Author:
Tom Kazmierski
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