Enhanced Techniques for Analog Circuits Design Using SVM Models

Abstract

In order to improve the relatively slow convergence of GA, in the presence of large search spaces, and reduce the high consuming time of evaluation functions in analog circuit design applications, this chapter will discuss the use of learning algorithms. These algorithms explore the successive generation of solutions, learn the tendency of the best optimization variables and will use this knowledge to predict future values. In other words, these techniques employ data mining theory, used to manage large databases and huge amount of internet information, to discover complex relationships among various factors and extract meaningful knowledge to improve the efficiency and quality of decision making. In this chapter a new hybrid optimization algorithm is presented together with a design methodology, which increases the efficiency on the analog circuit design cycle. This new algorithm combines an enhanced GA kernel with an automatic learning machine based on SVM model (GA-SVM) which efficiently guides the selection operator of the GA algorithm avoiding time-consuming SPICE evaluations of non-promising solutions. The SVM model is here defined as a classification model used to predict the feasibility region in the presence of large, non-linear and constraints search spaces that characterize analog design problems. The SVM modeling attempts to constraint the search space in order to accelerate the search towards the feasible region ensuring a proper operation of the circuit.