A Deep-Learning Framework for Predicting Congestion During FPGA Placement

The ability to quickly and accurately predict congestion has emerged as one of the most critical problems during placement. In this paper, we present DLCong, a deep learning congestion-estimation framework based on a convolutional encoder-decoder. Experimental results show that compared to MLCong, a state-of-the-art machine-learning based congestion-estimation model, DLCong achieves an almost 9% improvement in congestion accuracy, while exhibiting inference times of a few milliseconds. Moreover, the accuracy of DLCong scales better with increasing congestion compared to MLCong.