91 citations · 325 across the 11 of their papers we have counts for
15 papers
Privacy-Preserving Representation Learning on Graphs: A Mutual Information Perspective
Binghui Wang, Jiayi Guo, Ang Li +2
Learning with graphs has attracted significant attention recently. Existing representation learning methods on graphs have achieved state-of-the-art performance on various graph-re…
Provable Defense against Privacy Leakage in Federated Learning from Representation Perspective
Jingwei Sun, Ang Li, Binghui Wang +3
Federated learning (FL) is a popular distributed learning framework that can reduce privacy risks by not explicitly sharing private data. However, recent works demonstrated that sh…
Net2: A Graph Attention Network Method Customized for Pre-Placement Net Length Estimation
Zhiyao Xie, Rongjian Liang, Xiaoqing Xu +3
Net length is a key proxy metric for optimizing timing and power across various stages of a standard digital design flow. However, the bulk of net length information is not availab…
PowerNet: Transferable Dynamic IR Drop Estimation via Maximum Convolutional Neural Network
Zhiyao Xie, Haoxing Ren, Brucek Khailany +4
IR drop is a fundamental constraint required by almost all chip designs. However, its evaluation usually takes a long time that hinders mitigation techniques for fixing its violati…
FIST: A Feature-Importance Sampling and Tree-Based Method for Automatic Design Flow Parameter Tuning
Zhiyao Xie, Guan-Qi Fang, Yu-Hung Huang +7
Design flow parameters are of utmost importance to chip design quality and require a painfully long time to evaluate their effects. In reality, flow parameter tuning is usually per…
Fast IR Drop Estimation with Machine Learning
Zhiyao Xie, Hai Li, Xiaoqing Xu +2
IR drop constraint is a fundamental requirement enforced in almost all chip designs. However, its evaluation takes a long time, and mitigation techniques for fixing violations may…