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Circuit routing has been a historically challenging problem in designingelectronic systems such as very large-scale integration (VLSI) and printedcircuit boards (PCBs). The main challenge is that connecting a large number ofelectronic components under specific design rules involves a very large searchspace. Early solutions are typically designed with hard-coded heuristics, whichsuffer from problems of non-optimal solutions and lack of flexibility for newdesign needs. Although a few learning-based methods have been proposedrecently, they are typically cumbersome and hard to extend to large-scaleapplications. In this work, we propose a new algorithm for circuit routing,named Ranking Cost, which innovatively combines search-based methods (i.e., A*algorithm) and learning-based methods (i.e., Evolution Strategies) to form anefficient and trainable router. In our method, we introduce a new set ofvariables called cost maps, which can help the A* router to find out properpaths to achieve the global objective. We also train a ranking parameter, whichcan produce the ranking order and further improve the performance of ourmethod. Our algorithm is trained in an end-to-end manner and does not use anyartificial data or human demonstration. In the experiments, we compare with thesequential A* algorithm and a canonical reinforcement learning approach, andresults show that our method outperforms these baselines with higherconnectivity rates and better scalability.

Ranking Cost: Building An Efficient and Scalable Circuit Routing Planner with Evolution-Based Optimization