mlsanityData to 14 Oct 2026

Piano: A Multi-Constraint Pin Assignment-Aware Floorplanner

arXiv 2508.13161 ↗First posted 20 Jul 2025cs.AR cs.AIcited by 0

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Abstract (arXiv)

Floorplanning is a critical step in VLSI physical design, increasingly complicated by modern constraints such as fixed-outline requirements, whitespace removal, and the presence of pre-placed modules. In addition, the assignment of pins on module boundaries significantly impacts the performance of subsequent stages, including detailed placement and routing. However, traditional floorplanners often overlook pin assignment with modern constraints during the floorplanning stage. In this work, we introduce Piano, a floorplanning framework that simultaneously optimizes module placement and pin assignment under multiple constraints. Specifically, we construct a graph based on the geometric relationships among modules and their netlist connections, then iteratively search for shortest paths to determine pin assignments. This graph-based method also enables accurate evaluation of feedthrough and unplaced pins, thereby guiding overall layout quality. To further improve the design, we adopt a whitespace removal strategy and employ three local optimizers to enhance layout metrics under multi-constraint scenarios. Experimental results on widely used benchmark circuits demonstrate that Piano achieves an average 6.81% reduction in HPWL, a 13.39% decrease in feedthrough wirelength, a 16.36% reduction in the number of feedthrough modules, and a 21.21% drop in unplaced pins, while maintaining zero whitespace.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 15 references that are in the map. First posted on arXiv 20 Jul 2025.

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References · 3

  1. 2022MaskPlace: Fast Chip Placement via Reinforced Visual Representation Learningcited by 43
  2. 2023Macro Placement by Wire-Mask-Guided Black-Box Optimizationcited by 23
  3. 2024Benchmarking End-To-End Performance of AI-Based Chip Placement Algorithmscited by 14

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