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:我们公开、透明地实施和评估谷歌大脑在GitHub的宏观定位及其巡回培训的深度强化学习方法。我们在开放源代码中实施CT的关键“黑盒”元素,并澄清CT与自然文件之间的差异。开发并发布了关于开放增强功能的新测试框。我们评估了与多种替代宏观定位器一起的CT,并在GitHub公布了所有评估流程和相关脚本。我们的实验还包括学术混合规模定位基准,以及消化和稳定研究。我们评论了自然和CT的影响,以及未来研究的方向。