MA-Opt: Reinforcement Learning-based Analog Circuit Optimization using Multi-Actors

Abstract

Analog circuit design requires significant human efforts and expertise; therefore, electronic design automation (EDA) tools for analog design are needed. This study presents MA-Opt that is an analog circuit optimizer using reinforcement learning (RL)-inspired framework. MA-Opt using multiple actors is proposed to provide various predictions of optimized circuit designs in parallel. Sharing a specific memory that affects the loss function of network training is proposed to exploit multiple actors effectively, accelerating circuit optimization. Moreover, we devise a novel method to tune the most optimized design in previous simulations into a more optimized design. To demonstrate the efficiency of the proposed framework, MA-Opt was simulated for three analog circuits and the results were compared with those of other methods. The experimental results indicated the strength of using multiple actors with a shared elite solution set and the near-sampling method. Within the same number of simulations, while satisfying all given constraints, MA-Opt obtained minimum target metrics up to 24% better than DNN-Opt. Furthermore, MA-Opt obtained better Figure of Merits (FoMs) than DNN-Opt at the same runtime.