Global Placement with Deep Learning-Enabled Explicit Routability Optimization
Siting Liu1,a, Qi Sun1,b, Peiyu Liao1,c, Yibo Lin2 and Bei Yu1,d
1The Chinese University of Hong Kong
astliu@cse.cuhk.edu.hk
bqsun@cse.cuhk.edu.hk
cpyliao@cse.cuhk.edu.hk
dbyu@cse.cuhk.edu.hk
2Peking University
yibolin@pku.edu.cn
ABSTRACT
Placement and routing (PnR) is the most time-consuming part of the physical design flow. Recognizing the routing performance ahead of time can assist designers and design tools to optimize placement results in advance. In this paper, we propose a fully convolutional network model to predict congestion hotspots and then incorporate this prediction model into a placement engine, DREAMPlace, to get a more route-friendly result. The experimental results on ISPD2015 benchmarks show that with the superior accuracy of the prediction model, our proposed approach can achieve up to 9.05% reduction in congestion rate and 5.30% reduction in routed wirelength compared with the state-of-the-art.
