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.

翻译：布局在物理设计中至关重要，因为它极大地影响着功耗、性能和面积指标。近年来，诸如DREAMPlace等分析方法的进展已在全局布局中展现出令人瞩目的性能。然而，DREAMPlace存在一些局限性，例如在相同设置下可能无法保证可合法化的布局结果，从而导致结果脆弱且不可预测。本文指出主要问题在于陷入局部最优，并提出了一种混合优化框架，通过迭代扰动布局结果来有效逃离局部最优。该框架在两个流行基准测试上相较于现有最先进方法实现了显著改进。