MA-Opt: Reinforcement Learning-Based Analog Circuit Optimization Using Multi-Actors
Youngchang Choi, Sejin Park, Minjeong Choi, Kyongsu Lee, Seokhyeong Kang
Abstract
There is a need for electronic design automation (EDA) tools for analog circuit design since analog circuit design requires substantial human effort and expertise. Using reinforcement learning (RL)-inspired methodologies, this study presents MA-Opt, an analog circuit optimizer. We propose MA-Opt to provide multiple predictions of optimized circuit designs through the use of multiple actors. Multiple actors can be exploited effectively by sharing a memory that affects the loss function of network training, resulting in an accelerated optimization of circuits. Furthermore, we introduce a cooperative near-sampling method deploying a synergistic effect and then optimizing the design. The efficiency of MA-Opt was demonstrated by simulating three analog circuits and comparing the results to other methods. In the experiment, the use of multiple actors with a shared elite solution set and the cooperative near-sampling method proved to be effective. MA-Opt achieved minimum target metrics up to 34 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\%$</tex-math> </inline-formula> better than DNN-Opt within the same number of simulations while satisfying all given constraints. Moreover, at identical runtime, MA-Opt exhibited better Figure of Merits (FoMs) in comparison to DNN-Opt.