The Law of Attraction: Affinity-Aware Placement Optimization using Graph Neural Networks

Abstract

Placement is one of the most crucial problems in modern Electronic Design Automation (EDA) flows, where the solution quality is mainly dominated by on-chip interconnects. To achieve target closures, designers often perform multiple placement iterations to optimize key metrics such as wirelength and timing, which is highly time-consuming and computationally inefficient. To overcome this issue, in this paper, we present a graph learning-based framework named PL-GNN that provides placement guidance for commercial placers by generating cell clusters based on logical affinity and manually defined attributes of design instances. With the clustering information as a soft placement constraint, commercial tools will strive to place design instances in a common group together during global and detailed placements. Experimental results on commercial multi-core CPU designs demonstrate that our framework improves the default placement flow of Synopsys IC Compiler II (ICC2) by 3.9% in wirelength, 2.8% in power, and 85.7% in performance.