The sizing rules method for analog integrated circuit design
Proceedings of the 2001 IEEE/ACM international conference on Computer-aided design
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
A combined feasibility and performance macromodel for analog circuits
Proceedings of the 42nd annual Design Automation Conference
A theory of learning with similarity functions
Machine Learning
Sparse kernel SVMs via cutting-plane training
Machine Learning
Extraction and use of neural network models in automated synthesis of operational amplifiers
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Variability aware SVM macromodel based design centering of analog circuits
Analog Integrated Circuits and Signal Processing
| Hi-index | 0.00 |
Support vector machines (SVMs) have been widely used for creating fast and efficient performance macro-models for quickly predicting the performance parameters of analog circuits. These models have proved to be not only effective and fast but accurate also while predicting the performance. A kernel function is an integral part of SVM to obtain an optimized and accurate model. There is no formal way to decide, which kernel function is suited to a class of regression problem. While most commonly used kernels are radial basis function, polynomial, spline, multilayer perceptron; we have explored many other un-conventional kernel functions and report their efficacy and computational efficiency in this paper. These kernel functions are used with SVM regression models and these macromodels are tested on different analog circuits to check for their robustness and performance. We have used HSPICE for generating the set of learning data. Least Square SVM toolbox along with MATLAB was used for regression. The models which contained modified compositions of kernels were found to be more accurate and thus have lower root mean square error than those containing standard kernels. We have used different CMOS circuits varying in size and complexity as test vehicles--two-stage op amp, cascode op amp, comparator, differential op amp and voltage controlled oscillator.