We provide open, transparent implementation and assessment of Google Brain's
deep reinforcement learning approach to macro placement and its Circuit
Training (CT) implementation in GitHub. We implement in open source key
"blackbox" elements of CT, and clarify discrepancies between CT and Nature
paper. New testcases on open enablements are developed and released. We assess
CT alongside multiple alternative macro placers, with all evaluation flows and
related scripts public in GitHub. Our experiments also encompass academic
mixed-size placement benchmarks, as well as ablation and stability studies. We
comment on the impact of Nature and CT, as well as directions for future
research.

本研究提供了 Google Brain 在深度强化学习中应用于宏观布局及其电路训练的开放、透明的实现和评估。同时，我们实现了 CT 的关键 “黑盒” 元素，并澄清了 CT 和 Nature 论文之间的差异，并通过新的测试用例进行评估。最终我们与多个其他宏观布局工具进行了评估，评估流程和相关脚本都公开在 GitHub 上。本实验还涵盖了学术混合大小布局基准以及消融和稳定性研究。我们还对 Nature 和 CT 的影响以及未来研究方向进行了评论。