Presentation
To Tackle Cost-Skew Tradeoff: An Adaptive Learning Approach for Hub Node Selection
DescriptionIn chip design, skew is a pivotal factor that significantly influences the overall performance for routing. A major challenge is how to achieve an appropriate trade-off between the total wire-length cost and skew. Selecting hub nodes is an effective method to improve this cost-skew trade-off. In this paper, we propose a novel reinforcement learning-based method for hub node selection, where our key idea is leveraging an effective adaptive learning strategy. Moreover, our approach is particularly suitable for solving large-scale routing instances. The empirical results suggest that our method can achieve promising performance on both small-scale and large-scale clock nets, implying its potential practical significance in EDA.
Event Type
Research Manuscript
TimeWednesday, June 252:30pm - 2:45pm PDT
Location3006, Level 3
EDA
EDA7: Physical Design and Verification
Similar Presentations