Template-free symbolic performance modeling of analog circuits via canonical-form functions and genetic programming

Authors:
Trent McConaghy;Georges G. E. Gielen
Affiliations:
Solido Design Automation, Inc., Saskatoon, Canada and Microelectronics and Sensors, Department of Electrical Engineering, Katholieke Universiteit Leuven, Leuven, Belgium;ESAT-MICAS, Katholieke Universiteit Leuven, Leuven, Belgium
Venue:
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Year:
2009

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Abstract

This paper presents CAFFEINE, a method to automatically generate compact interpretable symbolic performance models of analog circuits with no prior specification of an equation template. CAFFEINE uses SPICE simulation data to model arbitrary nonlinear circuits and circuit characteristics. CAFFEINE expressions are canonical-form functions: product-of-sum layers alternating with sum-of-product layers, as defined by a grammar. Multiobjective genetic programming trades off error with model complexity. On test problems, CAFFEINE models demonstrate lower prediction error than posynomials, splines, neural networks, kriging, and support vector machines. This paper also demonstrates techniques to scale CAFFEINE to larger problems.