51 citations · 207 across the 17 of their papers we have counts for
7 papers · 1 filter
C-SAW: A Framework for Graph Sampling and Random Walk on GPUs
Santosh Pandey, Lingda Li, Adolfy Hoisie +2
Many applications require to learn, mine, analyze and visualize large-scale graphs. These graphs are often too large to be addressed efficiently using conventional graph processing…
SAPAG: A Self-Adaptive Privacy Attack From Gradients
Yijue Wang, Jieren Deng, Dan Guo +5
Distributed learning such as federated learning or collaborative learning enables model training on decentralized data from users and only collects local gradients, where data is p…
Efficient Transformer-based Large Scale Language Representations using Hardware-friendly Block Structured Pruning
Bingbing Li, Zhenglun Kong, Tianyun Zhang +4
Pre-trained large-scale language models have increasingly demonstrated high accuracy on many natural language processing (NLP) tasks. However, the limited weight storage and comput…
Against Membership Inference Attack: Pruning is All You Need
Yijue Wang, Chenghong Wang, Zigeng Wang +5
The large model size, high computational operations, and vulnerability against membership inference attack (MIA) have impeded deep learning or deep neural networks (DNNs) popularit…
EZLDA: Efficient and Scalable LDA on GPUs
Shilong Wang, Hang Liu, Anil Gaihre +1
LDA is a statistical approach for topic modeling with a wide range of applications. However, there exist very few attempts to accelerate LDA on GPUs which come with exceptional com…
FTRANS: Energy-Efficient Acceleration of Transformers using FPGA
Bingbing Li, Santosh Pandey, Haowen Fang +7
In natural language processing (NLP), the "Transformer" architecture was proposed as the first transduction model replying entirely on self-attention mechanisms without using seque…
GSoFa: Scalable Sparse Symbolic LU Factorization on GPUs
Anil Gaihre, Xiaoye S. Li, Hang Liu
Decomposing matrix A into a lower matrix L and an upper matrix U, which is also known as LU decomposition, is an essential operation in numerical linear algebra. For a sparse matri…