activity
20192026
most citedCommunication-Efficient Distributed Deep Learning: A Comprehensive Survey

90 citations · 250 across the 45 of their papers we have counts for

collaborators
Showing 2019Show all

5 papers · 1 filter

eess.IV2019

Computer-Aided Clinical Skin Disease Diagnosis Using CNN and Object Detection Models

Xin He, Shihao Wang, Shaohuai Shi +10

Skin disease is one of the most common types of human diseases, which may happen to everyone regardless of age, gender or race. Due to the high visual diversity, human diagnosis hi…

cs.LG2019★ 14 cited

Layer-wise Adaptive Gradient Sparsification for Distributed Deep Learning with Convergence Guarantees

Shaohuai Shi, Zhenheng Tang, Qiang Wang +2

To reduce the long training time of large deep neural network (DNN) models, distributed synchronous stochastic gradient descent (S-SGD) is commonly used on a cluster of workers. Ho…

cs.DC2019★ 6 cited

Benchmarking the Performance and Energy Efficiency of AI Accelerators for AI Training

Yuxin Wang, Qiang Wang, Shaohuai Shi +4

Deep learning has become widely used in complex AI applications. Yet, training a deep neural network (DNNs) model requires a considerable amount of calculations, long running time,…

cs.PF2019★ 5 cited

The Impact of GPU DVFS on the Energy and Performance of Deep Learning: an Empirical Study

Zhenheng Tang, Yuxin Wang, Qiang Wang +1

Over the past years, great progress has been made in improving the computing power of general-purpose graphics processing units (GPGPUs), which facilitates the prosperity of deep n…

cs.DC2019★ 9 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…