MaskPlace: Fast Chip Placement via Reinforced Visual Representation Learning
Authors: Yao Lai, Yao Mu, Ping Luo
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | extensive experiments on many public benchmarks show that Mask Place outperforms existing RL approaches in all key performance metrics |
| Researcher Affiliation | Academia | Yao Lai Yao Mu Ping Luo Department of Computer Science The University of Hong Kong {ylai,ymu,pluo}@cs.hku.hk |
| Pseudocode | No | The paper describes algorithms in Appendix A.3 but does not present them in a formal pseudocode or algorithm block. |
| Open Source Code | Yes | The deliverables are released at laiyao1.github.io/maskplace. |
| Open Datasets | Yes | We evaluate Mask Place in 24 circuit benchmarks selected from public datasets including the widely-used ISPD2005 [30], IBM benchmark suite [31], and Ariane RISC-V CPU design [32]. |
| Dataset Splits | No | The paper evaluates methods on public benchmarks but does not specify explicit training, validation, or test dataset splits (e.g., percentages or counts) or refer to predefined splits used for validation. |
| Hardware Specification | Yes | All of them are evaluated on one Ge Force RTX 3090 GPU, and the CPU version of DREAMPlace is allocated with 16 threads in a 16 CPU cores environment. |
| Software Dependencies | No | The paper mentions the PPO2 framework but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | The detailed network architectures are provided in Appendix A.4. The detailed training setup is provided in Appendix A.5. |