Mitigating Distribution Shift for Congestion Optimization in Global Placement
Su Zheng, Lancheng Zou, Siting Liu, Yibo Lin, Bei Yu, Martin C. S. Wong
Abstract
The placement and routing (PnR) flow plays a critical role in physical design. Poor routing congestion is a possible problem causing severe routing detours, which can lead to deteriorated timing performance or even routing failure. Deep-learning-based congestion prediction model is designed to guide the global placement process in previous work. However, the distribution shift problem in this method limits its performance. In this paper, we mitigate the distribution shift problem with a look-ahead mechanism inspired by optical flow prediction and an invariant feature space learning technique. With the proposed method, we can achieve better congestion prediction performance and less-congested placement results.