53 citations · 220 across the 27 of their papers we have counts for
5 papers · 1 filter
Accel-GCN: High-Performance GPU Accelerator Design for Graph Convolution Networks
Xi Xie, Hongwu Peng, Amit Hasan +7
Graph Convolutional Networks (GCNs) are pivotal in extracting latent information from graph data across various domains, yet their acceleration on mainstream GPUs is challenged by…
PASNet: Polynomial Architecture Search Framework for Two-party Computation-based Secure Neural Network Deployment
Hongwu Peng, Shanglin Zhou, Yukui Luo +9
Two-party computation (2PC) is promising to enable privacy-preserving deep learning (DL). However, the 2PC-based privacy-preserving DL implementation comes with high comparison pro…
Supporting Energy-Based Learning With An Ising Machine Substrate: A Case Study on RBM
Uday Kumar Reddy Vengalam, Yongchao Liu, Tong Geng +2
Nature apparently does a lot of computation constantly. If we can harness some of that computation at an appropriate level, we can potentially perform certain type of computation (…
Machine Learning Automated Approach for Enormous Synchrotron X-Ray Diffraction Data Interpretation
Xiaodong Zhao, YiXuan Luo, Juejing Liu +6
Manual analysis of XRD data is usually laborious and time consuming. The deep neural network (DNN) based models trained by synthetic XRD patterns are proved to be an automatic, acc…
RRNet: Towards ReLU-Reduced Neural Network for Two-party Computation Based Private Inference
Hongwu Peng, Shanglin Zhou, Yukui Luo +11
The proliferation of deep learning (DL) has led to the emergence of privacy and security concerns. To address these issues, secure Two-party computation (2PC) has been proposed as…