91 citations · 406 across the 17 of their papers we have counts for
10 papers · 1 filter
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…
Automatic Routability Predictor Development Using Neural Architecture Search
Chen-Chia Chang, Jingyu Pan, Tunhou Zhang +8
The rise of machine learning technology inspires a boom of its applications in electronic design automation (EDA) and helps improve the degree of automation in chip designs. Howeve…
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…
LotteryFL: Personalized and Communication-Efficient Federated Learning with Lottery Ticket Hypothesis on Non-IID Datasets
Ang Li, Jingwei Sun, Binghui Wang +4
Federated learning is a popular distributed machine learning paradigm with enhanced privacy. Its primary goal is learning a global model that offers good performance for the partic…
SparseTrain: Exploiting Dataflow Sparsity for Efficient Convolutional Neural Networks Training
Pengcheng Dai, Jianlei Yang, Xucheng Ye +5
Training Convolutional Neural Networks (CNNs) usually requires a large number of computational resources. In this paper, \textit{SparseTrain} is proposed to accelerate CNN training…
MVStylizer: An Efficient Edge-Assisted Video Photorealistic Style Transfer System for Mobile Phones
Ang Li, Chunpeng Wu, Yiran Chen +1
Recent research has made great progress in realizing neural style transfer of images, which denotes transforming an image to a desired style. Many users start to use their mobile p…
TIPRDC: Task-Independent Privacy-Respecting Data Crowdsourcing Framework for Deep Learning with Anonymized Intermediate Representations
Ang Li, Yixiao Duan, Huanrui Yang +2
The success of deep learning partially benefits from the availability of various large-scale datasets. These datasets are often crowdsourced from individual users and contain priva…