The nature of statistical learning theory
The nature of statistical learning theory
DATE '99 Proceedings of the conference on Design, automation and test in Europe
Behavioral synthesis of analog systems using two-layered design space exploration
Proceedings of the 36th annual ACM/IEEE Design Automation Conference
Optimal design of delta-sigma ADCs by design space exploration
Proceedings of the 39th annual Design Automation Conference
Architectural selection of A/D converters
Proceedings of the 40th annual Design Automation Conference
Performance Modeling of Analog Integrated Circuits Using Least-Squares Support Vector Machines
Proceedings of the conference on Design, automation and test in Europe - Volume 1
Proceedings of the 2003 IEEE/ACM international conference on Computer-aided design
VLSID '05 Proceedings of the 18th International Conference on VLSI Design held jointly with 4th International Conference on Embedded Systems Design
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Optimal design of a CMOS op-amp via geometric programming
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Extraction and use of neural network models in automated synthesis of operational amplifiers
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
WATSON: design space boundary exploration and model generation for analog and RFIC design
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Proceedings of the 2009 International Conference on Computer-Aided Design
Analog Integrated Circuits and Signal Processing
Comparison of modeling techniques in circuit variability analysis
International Journal of Numerical Modelling: Electronic Networks, Devices and Fields
Variability aware SVM macromodel based design centering of analog circuits
Analog Integrated Circuits and Signal Processing
Operating-point driven formulation for analog computer-aided design
Analog Integrated Circuits and Signal Processing
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The need to reuse the performance macromodels of an analog circuit topology challenges existing regression based modeling techniques. A model of good reusability should have a number of independent design parameters and each parameter can vary in a large numeric range. On the other hand, these requirements can cause a large percentage of functionally incorrect designs in the design space and thus results in a sparse feasible design space. They also complicate the mathematical relationship between the performance parameters and the design parameters. In order to tackle these challenges, this paper presents a combined feasibility and performance macromodel based on Support Vector Machines (SVMs). The feasibility model identifies the feasible designs that satisfy the design constraints. The performance macromodel is valid for feasible designs. Feasibility macromodeling is formulated as a classification problem while performance macromeling as a regression problem. An active learning scheme [5] has been applied to improve the accuracy of the feasibility model much faster than only using uniformly distributed designs in the entire design space. Our experiment shows that the performance macromodels in the feasible design space are more accurate and faster to construct and evaluate than performance macromodels in the entire design space without functional or performance constraints considered.