Reinforcement Learning within Tree Search for Fast Macro Placement
Authors: Zijie Geng, Jie Wang, Ziyan Liu, Siyuan Xu, Zhentao Tang, Mingxuan Yuan, Jianye Hao, Yongdong Zhang, Feng Wu
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on commonly used benchmarks demonstrate that Efficient Place achieves remarkable placement quality within a short timeframe, outperforming recent state-of-the-art approaches. |
| Researcher Affiliation | Collaboration | 1CAS Key Laboratory of Technology in GIPAS & Mo E Key Laboratory of Brain-inspired Intelligent Perception and Cognition, University of Science and Technology of China 2Noah s Ark Lab, Huawei, China 3Tianjin University, Tianjin, China. |
| Pseudocode | Yes | Algorithm 1 Efficient Place |
| Open Source Code | Yes | Our code is available at https://github.com/MIRALab-USTC/AI4EDA-Efficient Place.git. |
| Open Datasets | Yes | Following the previous studies (Lai et al., 2022; 2023; Shi et al., 2023), we evaluate these methods on the commonly used ISPD2005 benchmark (Nam et al., 2005) |
| Dataset Splits | No | The paper describes experimental hyperparameters and training loop details, but does not explicitly provide training/validation/test dataset splits with specific percentages or counts. |
| Hardware Specification | Yes | All the experiments are conducted on a single machine with NVidia Ge Force GTX 3090 GPUs and Intel(R) Xeon(R) E5-2667 v4 CPUs 3.20GHz. |
| Software Dependencies | No | The paper references algorithms and tools like PPO and DREAMPlace but does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | For a complete understanding of Efficient Place s configuration, the specific hyperparameters are listed in Table 2. |