---
pretty_name: PPAPlace-5000
license: other
size_categories:
- 1K<n<10K
tags:
- electronic-design-automation
- chip-placement
- physical-design
- ppa-prediction
- timing-analysis
- graph-neural-networks
- dreamplace
- openroad
- iccad-2026
configs:
- config_name: default
  data_files:
  - split: train
    path: samples.csv
---

# PPAPlace-5000

**The official training dataset for _PPAPlace: Differentiable Cross-Stage
Objectives for Chip Placement Optimization_, accepted at the 2026 IEEE/ACM
International Conference on Computer-Aided Design (ICCAD 2026), by Ruogu Chen
and Jie Han of the University of Alberta.**

[PPAPlace paper on arXiv](https://arxiv.org/abs/2608.13790)
| [PPAPlace code and reproduction workflow](https://github.com/ValleyC/PPAPlace)
| [Download files](https://huggingface.co/datasets/ValleyC/PPAPlace-5000/tree/main)
| [Citation](#citation)

PPAPlace learns differentiable placement objectives from downstream physical
design outcomes rather than relying on wirelength alone. PPAPlace-5000 pairs
complete mixed-size DREAMPlace placements with post-global-routing timing,
power, and area labels produced by a consistent ChiPBench/OpenROAD flow.

The dataset supports research on post-route PPA prediction, placement ranking,
cross-circuit generalization, learned placement objectives, and machine
learning for electronic design automation.

## At a glance

| | |
| --- | --- |
| Training pairs | **5,000** |
| Circuits | **10** open ChiPBench designs |
| Placements per circuit | **500** |
| Placement representation | Complete mixed-size DEF |
| Post-GRT targets | WNS, TNS, total power, core area |
| Paper | Accepted at **ICCAD 2026** |
| Paper preprint | [`arXiv:2608.13790`](https://arxiv.org/abs/2608.13790) |
| Compressed download | **8.47 GiB** |

Every pair consists of one final placement DEF and its matching OpenROAD label
JSON. `samples.csv` and `samples.jsonl` provide a lightweight 5,000-row index
with archive paths, labels, generation parameters, and file sizes.

## Why this dataset

- **Cross-stage supervision:** labels come from post-global-routing analysis,
  not a placement-only proxy.
- **Complete placement state:** DEFs contain macros and standard cells, which
  enables full mixed-size spatial features.
- **Controlled diversity:** placements come from deterministic randomized
  DREAMPlace configurations and contain distinct macro placements.
- **Balanced circuits:** every circuit contributes exactly 500 pairs.
- **Ready for training:** the sample indexes expose the target values and the
  corresponding archive members directly.

## Circuits

| Circuit | Placements | Labels | Archive size |
| --- | ---: | ---: | ---: |
| `bp_be12` | 500 | 500 | 0.20 GiB |
| `bp_fe` | 500 | 500 | 0.17 GiB |
| `bp_multi` | 500 | 500 | 0.90 GiB |
| `dft68` | 500 | 500 | 0.22 GiB |
| `ethernet` | 500 | 500 | 0.18 GiB |
| `isa_npu` | 500 | 500 | 4.97 GiB |
| `mor1kx` | 500 | 500 | 0.44 GiB |
| `or1200` | 500 | 500 | 0.14 GiB |
| `swerv_wrapper43` | 500 | 500 | 0.54 GiB |
| `vga_lcd` | 500 | 500 | 0.70 GiB |

## Labels and sample index

| PPAPlace target | OpenROAD field | Meaning |
| --- | --- | --- |
| `wns` | `globalroute__timing__setup__ws` | Worst setup slack |
| `tns` | `globalroute__timing__setup__tns` | Total negative setup slack |
| `power` | `globalroute__power__total` | Estimated total power |
| `area` | `globalroute__design__core__area` | Core area |

The root `samples.csv` is directly previewable on Hugging Face. Each row records
the circuit, configuration ID, archive member paths, file sizes, DREAMPlace
hyperparameters, random seed, and post-GRT labels.

## Download

Install the Hugging Face client and `zstd`:

```bash
python -m pip install -U huggingface_hub
```

Download the complete release:

```bash
hf download ValleyC/PPAPlace-5000 \
  --repo-type dataset \
  --local-dir data/PPAPlace-5000
```

Or download one circuit and the sample index:

```bash
hf download ValleyC/PPAPlace-5000 \
  data/bp_fe.tar.zst samples.csv \
  --repo-type dataset \
  --local-dir data/PPAPlace-5000
```

Programmatic download is also supported:

```python
from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="ValleyC/PPAPlace-5000",
    repo_type="dataset",
    local_dir="data/PPAPlace-5000",
    allow_patterns=[
        "README.md",
        "samples.csv",
        "data/bp_fe.tar.zst",
    ],
)
```

## Extract and train PPAPlace

Each archive expands to one circuit directory. Build the directory structure
expected by PPAPlace as follows:

```bash
mkdir -p data/ppaplace/dreamplace
tar --use-compress-program=unzstd \
  -xf data/PPAPlace-5000/data/bp_fe.tar.zst \
  -C data/ppaplace/dreamplace
```

Extract all ten archives under `data/ppaplace/dreamplace/`. Obtain the matching
LEF/design inputs from
[ChiPBench](https://github.com/MIRALab-USTC/ChiPBench), then link or copy its
per-circuit data as `data/ppaplace/chipbench/`:

```text
data/ppaplace/
|-- chipbench/
|   `-- bp_fe/                  # ChiPBench LEF/design files
`-- dreamplace/
    `-- bp_fe/
        |-- cfg_001_final.def
        |-- ...
        `-- grt_jsons/
            |-- cfg_001_final_grt.json
            `-- ...
```

Clone and install PPAPlace, then train on the complete release:

```bash
git clone https://github.com/ValleyC/PPAPlace.git
cd PPAPlace
python -m pip install -e .

python scripts/train.py \
  --data_root /absolute/path/to/data/ppaplace \
  --circuits bp_fe,bp_be12,isa_npu,bp_multi,or1200,swerv_wrapper43,vga_lcd,ethernet,dft68,mor1kx \
  --epochs 200 \
  --save checkpoints/ppaplace.pt
```

## How the corpus was generated

For every circuit, the release workflow sampled 1,000 deterministic
DREAMPlace configurations. It ran mixed-size global placement, macro
legalization, and a standard-cell-only placement pass, followed by the
ChiPBench/OpenROAD post-GRT flow. The selector retained exactly 500 candidates
with complete DEF/label pairs and different macro placements.

Macros are marked `FIXED` only during the third pass so standard cells can be
placed around that candidate's legalized macro solution. Macro coordinates are
not fixed across samples. The final corpus checks confirmed different combined
model inputs, non-collapsed spatial channels, and measurable movement by every
macro.

The generation and selection tools are published in the
[PPAPlace repository](https://github.com/ValleyC/PPAPlace).

## Provenance and license

This dataset is marked `license: other` because its generated placement DEFs
derive from ChiPBench benchmark inputs and no new blanket license is asserted
over third-party design material. ChiPBench's top-level BSD-3-Clause notice is
reproduced in `THIRD_PARTY_NOTICES.md`; individual benchmark designs may retain
additional upstream notices. DREAMPlace and OpenROAD are generation tools and
are not redistributed in these archives.

Please review `THIRD_PARTY_NOTICES.md` before redistribution. Cite ChiPBench,
DREAMPlace, OpenROAD, PPAPlace, and relevant upstream benchmark sources when
using the release.

## Citation

Please cite both the PPAPlace paper and this dataset:

```bibtex
@article{chen2026ppaplace,
  title   = {{PPAPlace}: Differentiable Cross-Stage Objectives for Chip Placement Optimization},
  author  = {Chen, Ruogu and Han, Jie},
  journal = {arXiv preprint arXiv:2608.13790},
  year    = {2026},
  note    = {Accepted at the 2026 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)},
  url     = {https://arxiv.org/abs/2608.13790}
}

@dataset{chen2026ppaplace5000,
  title     = {{PPAPlace-5000}: Post-Global-Routing Training Data for Learned Chip Placement},
  author    = {Chen, Ruogu and Han, Jie},
  year      = {2026},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/datasets/ValleyC/PPAPlace-5000}
}
```