# maskplace
**Repository Path**: kiloGrand/maskplace
## Basic Information
- **Project Name**: maskplace
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-01-07
- **Last Updated**: 2025-01-07
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
## MaskPlace: Fast Chip Placement via Reinforced Visual Representation Learning
A new chip placement method based on visual representation learning.
### Publication
Lai, Yao, Yao Mu, and Ping Luo. "Maskplace: Fast chip placement via reinforced visual representation learning." Advances in Neural Information Processing Systems 35 (2022): 24019-24030. (NeurIPS 2022, **spotlight**)
[paper](https://arxiv.org/pdf/2211.13382.pdf)
### Usage
You can start easily by using the following script.
```
cd maskplace
python PPO2.py
```
### Parameter
- **gamma** Decay factor.
- **seed** Random seed.
- **disable_tqdm** Whether to disable the progress bar.
- **lr** Learning rate.
- **log-interval** Interval between training status logs.
- **pnm** Number of place modules for each placement trajectory.
- **benchmark** Circuit benchmark.
- **soft_coefficient** Whether to constriant the actions based on the wiremask.
- **batch_size** Batch size.
- **is_test** Testing mode based on the trained agent.
- **save_fig** Whether to save placement figures.
### Benchmark
The repo has provided the benchmark *adaptec1* and *ariane*. For other benchmarks, you can download them by the following the link:
http://www.cerc.utexas.edu/~zixuan/ispd2005dp.tar.xz
### Dependency
- [Python](https://www.python.org/) >= 3.9
- [Pytorch](https://pytorch.org/) >= 1.10
- Other versions may also work, but not tested
- [gym](https://www.gymlibrary.dev/index.html) >= 0.21.0
- [matplotlib](https://matplotlib.org/) >= 3.7.1
- [tqdm](https://tqdm.github.io/)
- [protobuf](https://pypi.org/project/protobuf/) (for benchmark *ariane*)
### Citation
If you find our paper/code useful in your research, please cite
```
@article{lai2022maskplace,
title={Maskplace: Fast chip placement via reinforced visual representation learning},
author={Lai, Yao and Mu, Yao and Luo, Ping},
journal={Advances in Neural Information Processing Systems},
volume={35},
pages={24019--24030},
year={2022}
}
```
### The placement process animation
Benchmark: Bigblue3
|Placement| Pos Mask t | Wire Mask t |
|---|---|---|
|
|
|
|
| View Mask | Pos Mask t+1 | Wire Mask t+1 |
|
|
| 
|
### Standard Cell Placement
Fix macros and use DREAMPlace (classic optimization-based method) to place standard cells.
|adaptec2| adaptec4 | bigblue3 |
|---|---|---|
|
|
|
|
### Full Benchmark demonstration
|Benchmark| DREAMPlace | Graph | DeepPR | MaskPlace |
|---|---|---|---|---|
|adaptec1|
|
|
|
|
|HPWL (105)|17.94|26.05|21.36|6.57|
|Wirel (105)|19.24|28.54|25.64|7.36|
|Overlap|0.34%|1.89%|32.03%|0|
|adaptec2|
|
| 
|
|
|HPWL (105)|135.32|359.35|197.13|79.98|
|Wirel (105)|140.91|381.64|205.78|83.59|
|Overlap|0.16%|1.54%|49.10%|0|
|adaptec3|
|
| 
|
|
|HPWL (105)|112.28|392.66|340.29|79.33|
|Wirel (105)|119.23|409.37|372.02|85.28|
|Overlap|0|1.26%|29.10%|0|
|adaptec4|
|
| 
|
|
|HPWL (105)|37.77|152.89|243.12|75.75|
|Wirel (105)|47.90|179.43|290.14|88.87|
|Overlap|0|7.43%|19.29%|0|
|bigblue1|
|
| 
|
|
|HPWL (105)|2.50|8.32|20.49|2.42|
|Wirel (105)|3.41|10.00|25.68|3.14|
|Overlap|0|2.48%|9.33%|0|
|bigblue3|
|
| 
|
|
|HPWL (105)|104.05|345.49|439.09|82.61|
|Wirel (105)|107.58|373.33|517.86|88.51|
|Overlap|8.06%|0.80%|85.23%|0|
|ariane|
|
| 
|
|
|HPWL (105)|20.30|16.83|51.43|14.86|
|Wirel (105)|21.72|18.48|55.85|15.80|
|Overlap|0.78%|3.72%|38.91%|1.94%|