Late breaking results: Analog circuit generator based on deep neural network enhanced combinatorial optimization

Abstract

A deep neural network (DNN) based stochastic combinatorial optimization framework is presented that can find the optimal sizing of circuits in a sample-efficient manner. This sample efficiency allows us to unify this framework with generator-based tools like Berkeley Analog Generator (BAG) [1] to directly optimize layout, given the high level circuit specifications. We use this tool to design an optical link receiver layout, satisfying high-level design specifications, using post-layout simulations of only 348 design instances. Compared to an evolutionary algorithm without our DNN-based discriminator, our framework improves the sample efficiency and run time by more than 200x.