发表机构
LTCI, Télécom Paris, Institut Polytechnique de Paris; Arteris IP（LTCI，巴黎电信学院，巴黎综合理工学院; Arteris IP公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出分层强化学习框架，通过开关扩展、放置与路由细化三操作保持布线有效性，用Gumbel MCTS神经搜索提升EDA联合布线与开关放置的解质量。
AI 中文摘要
布线与开关放置是芯片设计中的基本组合优化问题，需要在严格的结构、几何和逻辑约束下，对布线拓扑和物理放置进行联合优化。现有方法通常依赖于精心设计的启发式算法，这些算法融入了强烈的问题特定偏差，以在可能设计的巨大空间中导航。在本工作中，我们引入了一个分层强化学习框架，用于在逻辑通信路由层面进行联合布线与开关放置。从一个最小布线图开始，我们的方法通过三个耦合操作逐步构建表达力越来越强的解决方案：开关扩展、开关放置和路由细化。这些操作通过构造保持布线有效性，将探索限制在可行配置中，其中每个通信的发起者-目标对都有一条分配的无环路由。我们使用Gumbel蒙特卡洛树搜索来探索诱导的解决方案空间，表明神经引导搜索显著提高了解决方案质量，优于非学习优化方法。此外，跨布局的预训练为在未见实例上的微调提供了强初始化。
英文摘要
Routing and switch placement are fundamental combinatorial optimization problems in chip design, requiring the joint optimization of routing topology and physical placement under strict structural, geometric and logical constraints. Existing approaches typically rely on carefully engineered heuristics that incorporate strong problem-specific biases to navigate the enormous space of possible designs. In this work, we introduce a hierarchical reinforcement learning framework for joint routing and switch placement at the level of logical communication routes. Starting from a minimal routing graph, our method progressively constructs increasingly expressive solutions through three coupled operations: switch expansion, switch placement, and route refinement. These operations preserve routing validity by construction, restricting exploration to feasible configurations where every communicating initiator-target pair has one assigned loop-free route. We explore the induced solution space using Gumbel Monte Carlo Tree Search, showing that neural-guided search substantially improves solution quality over non-learning optimization methods. Furthermore, pretraining across floorplans provides a strong initialization for fine-tuning on unseen instances.
