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DTSTAMP:20260402T024534Z
LOCATION:3006\, Level 3
DTSTART;TZID=America/Los_Angeles:20250625T143000
DTEND;TZID=America/Los_Angeles:20250625T144500
UID:dac_DAC 2025_sess143_RESEARCH1512@linklings.com
SUMMARY:To Tackle Cost-Skew Tradeoff: An Adaptive Learning Approach for Hu
 b Node Selection
DESCRIPTION:Lin Chen, Guowei Sun, Qiming Huang, and Hu Ding (University of
  Science and Technology of China)\n\nIn chip design, skew is a pivotal fac
 tor that significantly influences the overall performance for routing. A m
 ajor challenge  is how to achieve an appropriate trade-off between the tot
 al wire-length cost and skew. Selecting hub nodes is an effective method t
 o improve this cost-skew trade-off. In this paper, we propose a novel rein
 forcement learning-based method for hub node selection, where our key idea
  is leveraging an effective adaptive learning strategy. Moreover, our appr
 oach  is particularly suitable for solving large-scale routing instances. 
 The empirical results suggest that our method can achieve promising perfor
 mance on both small-scale and large-scale clock nets, implying its potenti
 al  practical significance in EDA.\n\nTopics: EDA\n\nTracks: EDA7: Physica
 l Design and Verification\n\nSession Chairs: Igor Markov (Synopsys) and Da
 vid Chinnery (Siemens)\n\n
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