FloorSet -- a VLSI Floorplanning Dataset with Design Constraints of Real-World SoCs
<|reference_start|>FloorSet -- a VLSI Floorplanning Dataset with Design Constraints of Real-World SoCs: Floorplanning for systems-on-a-chip (SoCs) and its sub-systems is a crucial and non-trivial step of the physical design flow. It represents a difficult combinatorial optimization problem. A typical large scale SoC with 120 partitions generates a search-space of nearly 10E250. As novel machine learning (ML) approaches emerge to tackle such problems, there is a growing need for a modern benchmark that comprises a large training dataset and performance metrics that better reflect real-world constraints and objectives compared to existing benchmarks. To address this need, we present FloorSet -- two comprehensive datasets of synthetic fixed-outline floorplan layouts that reflect the distribution of real SoCs. Each dataset has 1M training samples and 100 test samples where each sample is a synthetic floor-plan. FloorSet-Prime comprises fully-abutted rectilinear partitions and near-optimal wire-length. A simplified dataset that reflects early design phases, FloorSet-Lite comprises rectangular partitions, with under 5 percent white-space and near-optimal wire-length. Both datasets define hard constraints seen in modern design flows such as shape constraints, edge-affinity, grouping constraints, and pre-placement constraints. FloorSet is intended to spur fundamental research on large-scale constrained optimization problems. Crucially, FloorSet alleviates the core issue of reproducibility in modern ML driven solutions to such problems. FloorSet is available as an open-source repository for the research community.<|reference_end|>
@article{mallappa2024floorset,
    title={FloorSet -- a VLSI Floorplanning Dataset with Design Constraints of
  Real-World SoCs},
    author={Uday Mallappa, Hesham Mostafa, Mikhail Galkin, Mariano Phielipp,
  Somdeb Majumdar},
    journal={arXiv preprint arXiv:2405.05480},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.05480},
    primaryClass={cs.AR cs.AI cs.LG}
}