150 citations · 310 across the 71 of their papers we have counts for
5 papers · 1 filter
Block-Skim: Efficient Question Answering for Transformer
Yue Guan, Zhengyi Li, Jingwen Leng +3
Transformer models have achieved promising results on natural language processing (NLP) tasks including extractive question answering (QA). Common Transformer encoders used in NLP…
Characterizing and Demystifying the Implicit Convolution Algorithm on Commercial Matrix-Multiplication Accelerators
Yangjie Zhou, Mengtian Yang, Cong Guo +5
Many of today's deep neural network accelerators, e.g., Google's TPU and NVIDIA's tensor core, are built around accelerating the general matrix multiplication (i.e., GEMM). However…
Dubhe: Towards Data Unbiasedness with Homomorphic Encryption in Federated Learning Client Selection
Shulai Zhang, Zirui Li, Quan Chen +3
Federated learning (FL) is a distributed machine learning paradigm that allows clients to collaboratively train a model over their own local data. FL promises the privacy of client…
ZIPPER: Exploiting Tile- and Operator-level Parallelism for General and Scalable Graph Neural Network Acceleration
Zhihui Zhang, Jingwen Leng, Shuwen Lu +5
Graph neural networks (GNNs) start to gain momentum after showing significant performance improvement in a variety of domains including molecular science, recommendation, and trans…
Dual-side Sparse Tensor Core
Yang Wang, Chen Zhang, Zhiqiang Xie +3
Leveraging sparsity in deep neural network (DNN) models is promising for accelerating model inference. Yet existing GPUs can only leverage the sparsity from weights but not activat…