PC-Opt: Partition and Conquest-based Optimizer using Multi-Agents for Complex Analog Circuits

Recent research in electronic design automation (EDA) tools has focused on utilizing artificial intelligence (AI) for sizing analog circuit designs. Still, there has been a lack of focus on optimizing complex analog circuits. To optimize complex analog circuits within a few circuit simulations, we propose a partition-and-conquest-based optimizer (PC-Opt). PC-Opt assigns distinct actor-critic roles within a multi-agent system, facilitating the partitioning of complex analog circuits and conquering their optimization challenges. Partial differential training is developed for the proper prediction of each actor, which merges each other and then predicts the optimized entire circuit. To generate a compact and non-biased dataset for network training, a concentrated sampling method is devised. Experimental results on three circuits demonstrate the effectiveness of PC-Opt.