ChiPFormer: Transferable Chip Placement via Offline Decision Transformer
Authors: Yao Lai, Jinxin Liu, Zhentao Tang, Bin Wang, Jianye Hao, Ping Luo
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | extensive experiments on 32 chip circuits demonstrate that Chi PFormer achieves significantly better placement quality while reducing the runtime by 10 compared to recent state-of-the-art approaches in both public benchmarks and realistic industrial tasks. |
| Researcher Affiliation | Collaboration | 1 Department of Computer Science, The University of Hong Kong, Hong Kong 2 Shanghai AI Laboratory, China 3 Huawei Noah s Ark Lab, China 4 Zhejiang University, China 5 Tianjin University, China . |
| Pseudocode | Yes | The corresponding pseudo-code is shown in Appendix Algo. 1. |
| Open Source Code | Yes | The deliverables are released at sites.google.com/view/chipformer/home. |
| Open Datasets | Yes | To facilitate future research, we have released our collected offline dataset. ... The dataset is shared on Google drive. |
| Dataset Splits | No | The paper discusses training on offline data and finetuning on unseen circuits but does not specify typical train/validation/test dataset splits (e.g., percentages or sample counts) for a single dataset. |
| Hardware Specification | Yes | Table 11: Hyper-parameters used in our experiments ... computing hardware CPU AMD Ryzen 9 5950X GPU 2 RTX 3090 |
| Software Dependencies | No | The paper mentions software like DREAMPlace, Mask Place, and GPT but does not provide specific version numbers for these or other ancillary software components. |
| Experiment Setup | Yes | Detailed model architecture and hyper-parameter settings are in Appendix A.5, Table 10 and 11. ... Table 11: Hyper-parameters used in our experiments |