Towards Timing-Driven Routing: An Efficient Learning Based Geometric Approach

Liying Yang, Guowei Sun, Hu Ding

Abstract: As the rapid increasing of the circuits complexity, it is urgent to develop efficient algorithmic techniques for EDA. In this paper, we consider the routing problem which is a key part for designing high-quality chips. In particular, we combine both the max path length and total wirelength for modeling our optimization objective, since the path delay often causes timing issue that can seriously degrade the whole routing efficiency (even if the total wirelength is small). Comparing with most of the previous works that only considering wirelength, the timing-driven routing objective is much more challenging to optimize. We propose an efficient learning-based approach together with several novel insights in geometry. For moderate-degree nets, our approach can yield a better smooth trade-off between the wirelength and max path length comparing with the state-of-the-art methods. For large-degree nets, we propose an elegant and easy-to-implement geometric data structure called “data-dependent polar quadtree” in the space; using this structure, we can successfully plug our learning-based approach into a divide & merge framework and the optimization quality over the whole instance can be well preserved.