Bridging the Initialization Gap: A Co-Optimization Framework for Mixed-Size Global Placement

TL;DR

This paper introduces a co-optimization framework that enhances global placement initialization by combining area-aware heuristics with fast point-based methods, significantly improving placement quality and speed.

Contribution

A novel lightweight co-optimization framework that bridges the gap between area-aware and fast initializers using graph-based heuristics and macro-schedule techniques.

Findings

Achieves up to 2.2% HPWL reduction over point-based initializers.

Runs approximately 100 times faster than existing area-aware initializers.

Consistently improves placement quality across multiple benchmarks.

Abstract

Global placement is a critical step with high computational complexity in VLSI physical design. Modern analytical placers formulate the placement problem as a nonlinear optimization, where initialization strongly affects both convergence behavior and final placement quality. However, existing initialization methods exhibit a trade-off: area-aware initializers account for cell areas but are computationally expensive and can dominate total runtime, while fast point-based initializers ignore cell area, leading to a modeling gap that impairs convergence and solution quality. We propose a lightweight co-optimization framework that bridges this initialization gap through two strategies. First, an area-hint refinement initializer incorporates heuristic cell area information into a signed graph signal by augmenting the netlist graph with virtual nodes and negative-weight edges, yielding an area-aware and spectrally smooth placement initialization. Second, a macro-schedule placement procedure progressively restores area constraints, enabling a smooth transition from the refined initializer to the full area-aware objective and producing high-quality placement results. We evaluate the framework on macro-heavy ISPD2005 academic benchmarks and two real-world industrial designs across two technology nodes (12 cases in total). Experimental results show that our method consistently improves half-perimeter wirelength (HPWL) over point-based initializers in 11 out of 12 cases, achieving up to 2.2% HPWL reduction, while running approximately 100 times faster than the state-of-the-art area-aware initializer.