MALT: ML Assisted Shallow-Light Tree Construction

Timing is a critical issue in electronic design automation (EDA). To reduce the delay of a net, an important strategy is to minimize the path lengths from the source to the sinks. However, minimizing the path lengths will inevitably sacrifice the total wirelength. To balance the two objectives, researchers use shallow-light tree (SLT) to model and optimize the problem. In this article, we introduce MALT, a novel approach that uses a neural network to guide the construction of Steiner shallow-light trees. The constructed trees are further refined by a dynamic programming-based branch merging algorithm, which improves the wirelength without sacrificing the path lengths of any sinks. Our experimental results demonstrate that the proposed framework achieves significant improvements over both state-of-the-art traditional SLT generation algorithms and existing machine learning enhanced methods.