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20152023
most citedSpeeding up Convolutional Neural Networks By Exploiting the Sparsity of Rectifier Units

33 citations · 84 across the 11 of their papers we have counts for

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Showing cs.DCShow all

8 papers · 1 filter

cs.DC2023

Stochastic Performance Analysis of Phase Decomposition in Hyperledger Fabric

Canhui Wang, Xiaowen Chu

Hyperledger Fabric is one of the most popular permissioned blockchain platforms. Although many existing works on the overall system performance of Hyperledger Fabric are available,…

cs.DC202310 cited

FusionAI: Decentralized Training and Deploying LLMs with Massive Consumer-Level GPUs

Zhenheng Tang, Yuxin Wang, Xin He +8

The rapid growth of memory and computation requirements of large language models (LLMs) has outpaced the development of hardware, hindering people who lack large-scale high-end GPU…

cs.DC20192 cited

GPU Accelerated Keccak (SHA3) Algorithm

Canhui Wang, Xiaowen Chu

Hash functions like SHA-1 or MD5 are one of the most important cryptographic primitives, especially in the field of information integrity. Considering the fact that increasing meth…

cs.DC20195 cited

GPU Accelerated AES Algorithm

Canhui Wang, Xiaowen Chu

It has been widely accepted that Graphics Processing Units (GPU) is one of promising schemes for encryption acceleration, in particular, the support of complex mathematical calcula…

cs.DC20199 cited

A Distributed Synchronous SGD Algorithm with Global Top- Sparsification for Low Bandwidth Networks

Shaohuai Shi, Qiang Wang, Kaiyong Zhao +4

Distributed synchronous stochastic gradient descent (S-SGD) has been widely used in training large-scale deep neural networks (DNNs), but it typically requires very high communicat…

cs.DC2018

A DAG Model of Synchronous Stochastic Gradient Descent in Distributed Deep Learning

Shaohuai Shi, Qiang Wang, Xiaowen Chu +1

With huge amounts of training data, deep learning has made great breakthroughs in many artificial intelligence (AI) applications. However, such large-scale data sets present comput…