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.
翻译:我们提供了谷歌脑神经网络强化学习应用于宏观布局的开放、透明的实现和评估,并在GitHub上实现了CT的关键“黑盒”元素,并澄清了CT与Nature论文之间的差异。我们开发并发布了新的基于开放数据的测试案例。我们将CT与多种替代宏观布局器进行了评估,并在GitHub上公开了所有评估流程和相关脚本。我们的实验还包括学术混合大小的布局基准测试以及消融和稳定性研究。我们评论了Nature和CT的影响,以及未来研究的方向。