发表机构
Harbin Institute of Technology（哈尔滨工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
RollPlace提出两阶段宏布局优化框架，结合机器学习与启发式初始布局，通过蒙特卡洛树搜索和推出机制改进布局，在ISPD 2005及19个基准上超越现有方法。
AI 中文摘要
近年来，强化学习（RL）在电子设计自动化（EDA）中的应用，特别是芯片布局方面，引起了广泛关注。虽然现有的基于机器学习（ML）的方法取得了显著进展，但它们主要侧重于一次性生成最优布局，往往产生的解决方案需要后续改进。为了解决这一局限性，我们提出了RollPlace，一个新颖且通用的宏布局框架。RollPlace采用两阶段优化策略：通过机器学习方法或基于启发式的策略生成初始布局解决方案，并通过调整初始阶段得出的特定宏来高效改进这些布局。该策略规避了传统基于RL的布局方法中固有的顺序生成约束。此外，RollPlace无缝集成了蒙特卡洛树搜索（MCTS）以平衡探索与利用，并采用推出机制进行高效的局部搜索。在ISPD 2005基准上的广泛实验表明，RollPlace优于最先进的方法。此外，基于OpenROAD在19个基准上的端到端实验结果表明，RollPlace在多个指标上表现出色。所提出的框架为解决现代芯片设计日益增长的复杂性提供了一个稳健且可扩展的解决方案。
英文摘要
The application of Reinforcement Learning (RL) in Electronic Design Automation (EDA), particularly for chip placement, has attracted considerable attention in recent years. While existing machine learning (ML)-based approaches have achieved notable progress, they predominantly focus on generating optimal layouts in a single attempt, often producing solutions that require subsequent refinement. To address this limitation, we propose RollPlace, a novel and generalized macro placement framework. RollPlace adopts a two-stage optimization strategy: generating initial placement solutions via machine learning methods or heuristic-based strategies, and refining these layouts efficiently by adjusting specific macros derived from the initial stage. This strategy circumvents the sequential generation constraints inherent in traditional RL-based placement methods. Furthermore, RollPlace seamlessly integrates Monte Carlo Tree Search (MCTS) to balance exploration and exploitation, and employs a rollout mechanism for efficient local search. Extensive experiments on the ISPD 2005 benchmark demonstrate that RollPlace outperforms state-of-the-art methods. Additionally, end-to-end experimental results based on OpenROAD across 19 benchmarks show that RollPlace excels in multiple metrics. The proposed framework offers a robust and scalable solution for addressing the growing complexity of modern chip design challenges.
Comments14 pages, 8 figures, 6 tables
Journal refIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 45, no. 7, pp. 3155-3168, July 2026
