Towards Multi-Objective Routing: A Novel Coreset-based Transfer Learning Framework

Xianglu Wang, Hu Ding

Abstract: Routing is a key stage in current IC design. Due to its hardness in optimization, several machine learning algorithms have been developed recently. But they often suffer from the issues like high time complexity for training and large demand of training data. Moreover, modern routing usually needs to consider various optimization objectives (e.g., the total wirelength or the maximum time delay). It is prohibitively expensive if we always train a new model from scratch to adapt each encountered new routing objective. In this paper, we introduce a novel Coreset-based Transfer Learning (CoTL) framework that can significantly reduce the training time and the amount of newly added training data. The key part of our framework relies on a novel sampling idea called "model-guided coreset", which can yield 5-8X reduction on the training time with preserving comparable routing quality to the state-of-the-art learning based approaches.

External IDs:dblp:conf/iccad/WangD25