30 citations · 38 across the 8 of their papers we have counts for
4 papers · 1 filter
ParasGB: A Graph Benchmark Suite for Parasitic Estimation on AMS Circuits
Jiajun Zou, Jiawei Liu, Ao Liu +8
As chip manufacturing processes advance to deep submicron nodes, parasitic interconnect effects increasingly dominate the performance of analog and mixed-signal (AMS) circuits and…
Transferable Parasitic Estimation via Graph Contrastive Learning and Label Rebalancing in AMS Circuits
Shan Shen, Shenglu Hua, Jiajun Zou +4
Graph representation learning on Analog-Mixed Signal (AMS) circuits is crucial for various downstream tasks, e.g., parasitic estimation. However, the scarcity of design data, the u…
Endowing Pre-trained Graph Models with Provable Fairness
Zhongjian Zhang, Mengmei Zhang, Yue Yu +3
Pre-trained graph models (PGMs) aim to capture transferable inherent structural properties and apply them to different downstream tasks. Similar to pre-trained language models, PGM…
Graph Foundation Models: Concepts, Opportunities and Challenges
Jiawei Liu, Cheng Yang, Zhiyuan Lu +8
Foundation models have emerged as critical components in a variety of artificial intelligence applications, and showcase significant success in natural language processing and seve…