Variability-Aware, Discrete Optimization for Analog Circuits

Abstract

This paper explores the use of discrete optimization techniques for variability-aware analog circuit synthesis, with an observation that the continuous design space can be effectively covered by a finite number of discrete points when parameter variation is present. Three algorithms are described that can leverage a discretized design space yet mitigate its dimensionality scaling problem: an isotropic discretization scheme, which can fill the neighborhood of any given point with only quadratically-increasing number of nearest neighbors placed at equal distances as the space dimension increases, a stochastic hill-climbing algorithm, which evaluates only a partial set of nearest neighbors yet finds the local optimum at the reduced cost, and an incremental Monte Carlo sampling algorithm, which draws the minimal number of Monte Carlo samples just enough to determine the superior design point during the local search process. These algorithms help in finding the optimal design of analog circuits efficiently. For instance, for a digitally-controlled oscillator example, its discretized design space consists of 5 565 907 points but the optimal point was found by evaluating only 40 design points and running only 21 Monte Carlo simulations per point in average (824 in total).