Simultaneous Reconnection Surgery Technique of Routing With Machine Learning-Based Acceleration

In global routing, both timing and routability are critical criteria to measure the performance of a design. However, these two objectives naturally conflict with each other during routing. In this paper, we propose reconnection approaches to fix timing. We first formulated a quadratic program (QP), which adjusts routing topologies of all the nets by only reconnecting critical sinks and takes congestion into consideration to tradeoff timing and routability objectives. A machine learning (ML)-based technique is applied to accelerate our algorithm, which offers a fast and effective way to solve the problem. By exploring more reconnection candidates, we then formulated a QP to reconnect any sink of a net and utilized a multilabel classifier to accelerate the process. The experimental results on ICCAD 2015 benchmarks show that our algorithms can achieve timing improvement with no significant degradation in routability and wirelength. With ML-based acceleration, our results can be obtained in almost negligible runtime.