Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
ChiPFormer: Transferable Chip Placement via Offline Decision Transformer
Authors: Yao Lai, Jinxin Liu, Zhentao Tang, Bin Wang, Jianye Hao, Ping Luo
ICML 2023 | Venue PDF | LLM Run Details | Input Tokens: 27,749 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,516 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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 |