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 embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Reinforcement Learning Policy as Macro Regulator Rather than Macro Placer
Authors: Ke Xue, Ruo-Tong Chen, Xi Lin, Yunqi Shi, Shixiong Kai, Siyuan Xu, Chao Qian
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our approach on the ISPD 2005 and ICCAD 2015 benchmark, comparing the global half-perimeter wirelength and regularity of our proposed method against several competitive approaches. Besides, we test the PPA performance using commercial software, showing that RL as a regulator can achieve significant PPA improvements. |
| Researcher Affiliation | Collaboration | 1National Key Laboratory for Novel Software Technology, Nanjing University 2School of Artificial Intelligence, Nanjing University 3Huawei Noah s Ark Lab |
| Pseudocode | No | The paper includes figures describing the architecture and masks (Figures 2 and 3) but does not provide any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at https://github.com/lamda-bbo/macro-regulator. |
| Open Datasets | Yes | We mainly use the ICCAD 2015 benchmark [14] as our test-bed... Besides, we also conduct experiments on ISPD 2005 benchmark [25] |
| Dataset Splits | No | The paper mentions 'pre-train' and 'test' splits (e.g., 'pre-train Mask Regulate and Mask Place on the first four chips... and test on the remaining four chips') but does not specify a separate validation dataset split with exact percentages or sample counts. |
| Hardware Specification | Yes | Device. CPU: Intel(R) Xeon(R) Gold 6430 GPU: 4 Ge Force RTX 4090 |
| Software Dependencies | No | The paper mentions using 'original implementations' for compared methods and lists some software with links in footnotes (e.g., DREAMPlace, Auto DMP, Wire Mask-EA, Mask Place), but it does not specify version numbers for general ancillary software components (e.g., Python, PyTorch, CUDA). |
| Experiment Setup | Yes | Table 4: Hyperparameters Configuration Value. Optimizer Adam, Learning rate 2.5e-3, Total episode 1000, Epoch for update 10, Batch size 64, Buffer capacity 5120, Clip ϵ 0.2, Clip gradient norm 0.5, Reward discount γ 0.95, Mask soft coefficient 1, DREAMPlace evaluation number 3, Trade-off coefficient α 0.7, Grid soft coefficient 4. |