Escaping Local Optima in Global Placement

TL;DR

This paper introduces a hybrid optimization framework that effectively escapes local optima in global placement problems, improving results over existing methods like DREAMPlace by perturbing placements iteratively.

Contribution

A novel hybrid framework that enhances global placement by escaping local optima through iterative perturbations, addressing limitations of current analytical methods.

Findings

Significant improvement over state-of-the-art methods

Effective in escaping local optima

Achieves better placement quality on benchmarks

Abstract

Placement is crucial in the physical design, as it greatly affects power, performance, and area metrics. Recent advancements in analytical methods, such as DREAMPlace, have demonstrated impressive performance in global placement. However, DREAMPlace has some limitations, e.g., may not guarantee legalizable placements under the same settings, leading to fragile and unpredictable results. This paper highlights the main issue as being stuck in local optima, and proposes a hybrid optimization framework to efficiently escape the local optima, by perturbing the placement result iteratively. The proposed framework achieves significant improvements compared to state-of-the-art methods on two popular benchmarks.