FloorSet -- a VLSI Floorplanning Dataset with Design Constraints of Real-World SoCs

TL;DR

FloorSet provides large-scale, realistic benchmark datasets for VLSI floorplanning, facilitating research on constrained optimization problems and improving reproducibility in machine learning approaches for SoC design.

Contribution

The paper introduces FloorSet, comprehensive synthetic datasets with real-world constraints for VLSI floorplanning, addressing reproducibility and scalability issues in ML-based optimization research.

Findings

Two datasets with 1 million training samples each

Inclusion of real-world design constraints in datasets

Open-source availability for research community

Abstract

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.