基于蒙特卡罗树搜索和深度神经网络的电路布线
Circuit Routing Using Monte Carlo Tree Search and Deep Neural Networks
摘要：
电路布线是设计电子系统中的基本问题，例如形成电子和计算机硬件的集成电路（IC）和印刷电路板（PCB）。与查找位置对之间的路径一样，电路布线生成导线迹线以连接电路组件的触点或引线。这是具有挑战性的，因为在密集和大量的电子元件之间寻找路径涉及非常大的搜索空间。现有的解决方案要么是手工设计的领域知识或定制特定的设计规则，因此，难以适应新的问题或设计需求。因此，非常需要通用的路由方法。在本文中，我们将电路布线建模为一个顺序决策问题，并解决它的蒙特卡罗树搜索（MCTS）与深度神经网络（DNN）引导推出。该方法可以很容易地扩展到具有更多布线约束和优化目标的布线情况。随机生成的单层电路上的实验显示了布线复杂电路的潜力。所提出的方法可以解决的问题，基准方法，如顺序A* 方法和李的算法不能解决，也可以优于香草MCTS方法。
英文摘要：
Circuit routing is a fundamental problem in designing electronic systems such as integrated circuits (ICs) and printed circuit boards (PCBs) which form the hardware of electronics and computers. Like finding paths between pairs of locations, circuit routing generates traces of wires to connect contacts or leads of circuit components. It is challenging because finding paths between dense and massive electronic components involves a very large search space. Existing solutions are either manually designed with domain knowledge or tailored to specific design rules, hence, difficult to adapt to new problems or design needs. Therefore, a general routing approach is highly desired. In this paper, we model the circuit routing as a sequential decision-making problem, and solve it by Monte Carlo tree search (MCTS) with deep neural network (DNN) guided rollout. It could be easily extended to routing cases with more routing constraints and optimization goals. Experiments on randomly generated single-layer circuits show the potential to route complex circuits. The proposed approach can solve the problems that benchmark methods such as sequential A* method and Lee's algorithm cannot solve, and can also outperform the vanilla MCTS approach.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
