Due to the increasing complexity of chip design, existing placement methods still have many shortcomings in dealing with macro cells coverage and optimization efficiency. Aiming at the problems of layout overlap, inferior performance, and low optimization efficiency in existing chip design methods, this paper proposes an end-to-end placement method, SRLPlacer, based on reinforcement learning. First, the placement problem is transformed into a Markov decision process by establishing the coupling relationship graph model between macro cells to learn the strategy for optimizing layouts. Secondly, the whole placement process is optimized after integrating the standard cell layout. By assessing on the public benchmark ISPD2005, the proposed SRLPlacer can effectively solve the overlap problem between macro cells while considering routing congestion and shortening the total wire length to ensure routability.

翻译：随着芯片设计复杂度的不断提升，现有布局方法在处理宏单元覆盖与优化效率方面仍存在诸多不足。针对现有芯片设计方法中存在的布局重叠、性能欠佳及优化效率低等问题，本文提出一种基于强化学习的端到端布局方法SRLPlacer。首先，通过建立宏单元间的耦合关系图模型，将布局问题转化为马尔可夫决策过程，以学习优化布局的策略。其次，在整合标准单元布局后对整个布局过程进行优化。通过在公开基准测试集ISPD2005上的评估，所提出的SRLPlacer能够有效解决宏单元间的重叠问题，同时兼顾布线拥塞并缩短总线长以确保可布线性。