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.

翻译：片上系统（SoC）及其子系统的布图规划是物理设计流程中关键且复杂的步骤，它代表了一个困难的组合优化问题。一个典型包含120个分区的大规模SoC会产生接近10E250的搜索空间。随着新颖的机器学习方法开始应对此类问题，业界日益需要一个现代基准数据集，它应包含大规模训练样本和能更好反映真实世界约束与目标的性能指标，以区别于现有基准。为满足这一需求，我们提出了FloorSet——两个综合性的合成固定轮廓布图布局数据集，其分布反映了真实SoC的特性。每个数据集包含100万个训练样本和100个测试样本，每个样本均为合成布图方案。FloorSet-Prime包含完全邻接的直角多边形分区及接近最优的线长；作为反映早期设计阶段的简化数据集，FloorSet-Lite包含矩形分区，其空白区域低于5%且具有接近最优的线长。两个数据集均定义了现代设计流程中的硬约束，包括形状约束、边缘亲和性约束、分组约束和预布局约束。FloorSet旨在推动大规模约束优化问题的基础研究，其关键价值在于缓解了现代机器学习方法在此类问题中可复现性的核心难题。FloorSet已作为开源资源向研究社区开放。