Multi-objective genetic approach for analog circuit sizing using SVM macro-model

Abstract

Analog circuit sizing is the task to determine the sizes of all components in the circuit during automated synthesis. Randomized combinatorial optimization algorithms are desired for quicker determination of a set of optimal sizes of the components. These algorithms require set of multiple performance parameters, for a very large number of sized circuits. Therefore the reduction in time required to estimate these performance parameters is also highly desired. For the purpose of estimation of performance parameters, we employ Support Vector Machine (SVM) based macro-models of analog circuits, instead of using SPICE simulation. These SVM macro-models are not only faster to evaluate, but use of efficient kernel functions has also made them almost as accurate as SPICE. In this paper, we report multi-objective genetic algorithm for simultaneous optimization of multiple performance parameters. We compute the Pareto optimal points for various performance parameters of a two-stage op-amp circuit in 180 nm technology. We perform SVM classification and regression using Least Square SVM toolbox with MATLAB. HSPICE was used to generate data-set from simulation of two-stage op-amp, which was used to train the SVM macro-model. The results pertaining to total time consumed in sizing task are very encouraging. We observed 'time taken' in one evaluation by SVM macromodel as compared to HSPICE is upto two order smaller, resulting in speed-up of approximately 20.