Escaping Local Optima in Global Placement
Ke Xue Xi Lin Yunqi Shi Shixiong Kai Siyuan Xu Chao Qian
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.