  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.
