Global Placement with Deep Learning-Enabled Explicit Routability Optimization

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.