Reinforcement Learning Guided Detailed Routing for Custom Circuits
Reinforcement Learning Guided Detailed Routing for Custom Circuits
复制标题
强化学习引导定制电路的详细布线
DOI：
10.1145/3569052.3571874
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Ren, Haoxing
中科院分区：
文献类型：
--
作者：
Chen, Hao;Hsu, Kai-Chieh;Turner, Walker J.;Wei, Po-Hsuan;Zhu, Keren;Pan, David Z.;Ren, Haoxing
详细布线是设计自动化中最繁琐、最复杂的过程，已成为先进制造节点布局自动化的决定性因素。尽管定制集成电路（IC）布线研究不断取得进展，但由于定制IC设计问题的高度复杂性，工业定制布局流程仍然是大量手动的。除了诸如线长最小化的常规设计目标之外，定制详细路由还必须适应附加约束（例如，路径匹配），使一个已经具有挑战性的过程变得更加困难。本文提出了一种新颖的定制电路详细布线框架，该框架利用深度强化学习来优化布线模式，同时考虑定制布线约束和工业设计规则。基于工业设计的全面布局后分析表明，我们的框架在处理指定的约束条件和生产签核质量布线解决方案的有效性。
Detailed routing is the most tedious and complex procedure in design automation and has become a determining factor in layout automation in advanced manufacturing nodes. Despite continuing advances in custom integrated circuit (IC) routing research, industrial custom layout flows remain heavily manual due to the high complexity of the custom IC design problem. Besides conventional design objectives such as wirelength minimization, custom detailed routing must also accommodate additional constraints (e.g., path-matching) across the analog/mixed-signal (AMS) and digital domains, making an already challenging procedure even more so. This paper presents a novel detailed routing framework for custom circuits that leverages deep reinforcement learning to optimize routing patterns while considering custom routing constraints and industrial design rules. Comprehensive post-layout analyses based on industrial designs demonstrate the effectiveness of our framework in dealing with the specified constraints and producing sign-off-quality routing solutions.