Exploring Efficient Kernel Functions for Support Vector Machine Based Feasibility Models for Analog Circuits

Support Vector Machines (SVMs) have been used as classifier to identify the feasible design space of analog circuits.A feasibility design space is defined as a multidimensional space in which every point representing a design satisfies all the design constraints. The minimum set of constraints is the one that ensures the correct functionality of the given circuit topology. Performance Macromodels that facilitates accelerated analog circuit synthesis are constructed and thereby valid only in the functionally correct design space. A kernel function is an integral part of the SVM and contributes in obtaining an optimized and accurate classifier. A kernel function serves as a separating function, a hypersurface which optimally separates input data into two classes involving minimal support vectors. The support vectors are data points in input space lying on kernel function hypersurface. There is no formal way to decide, which kernel function is suited to a class of classifier problem. While most commonly used kernels are Radial Basis Function (RBF), polynomial,spline, multilayer perception; we have explored many other un-conventional kernel functions and kernels composed through modifications on the some of the standard kernels functions. The classifiers using these new kernel functions have been tested on different analog circuits in order to identify the feasible design space. HSPICE has been used for generation of learning data.Least Square SVM toolbox interfaced with MATLAB was used for classification. We found that use of modified kernels improves classification accuracy and shortens classifier training time as well.