Placement is a critical step in modern chip design, aiming to determine the positions of circuit modules on the chip canvas. Recent works have shown that reinforcement learning (RL) can improve human performance in chip placement. However, such an RL-based approach suffers from long training time and low transfer ability in unseen chip circuits. To resolve these challenges, we cast the chip placement as an offline RL formulation and present ChiPFormer that enables learning a transferable placement policy from fixed offline data. ChiPFormer has several advantages that prior arts do not have. First, ChiPFormer can exploit offline placement designs to learn transferable policies more efficiently in a multi-task setting. Second, ChiPFormer can promote effective finetuning for unseen chip circuits, reducing the placement runtime from hours to minutes. Third, extensive experiments on 32 chip circuits demonstrate that ChiPFormer achieves significantly better placement quality while reducing the runtime by 10x compared to recent state-of-the-art approaches in both public benchmarks and realistic industrial tasks. The deliverables are released at https://sites.google.com/view/chipformer/home.

翻译：布局是现代芯片设计中的关键步骤，旨在确定电路模块在芯片画布上的位置。近期研究表明，强化学习（RL）能够提升芯片布局中的人类表现水平。然而，这种基于强化学习的方法存在训练时间长、对未见芯片电路迁移能力差的问题。为应对这些挑战，我们将芯片布局问题形式化为离线强化学习任务，并提出ChiPFormer——一种能够从固定离线数据中学习可迁移布局策略的方法。相比于现有技术，ChiPFormer具有多项优势：第一，它能够利用离线布局设计，在多任务场景中更高效地学习可迁移策略；第二，它可促进对未见芯片电路的有效微调，将布局运行时间从数小时缩短至分钟级；第三，在32个芯片电路上的大量实验表明，无论公共基准测试还是真实工业任务，ChiPFormer均能以10倍于最新方法的运行效率实现显著更优的布局质量。相关成果已发布于https://sites.google.com/view/chipformer/home。