Results 41 to 50 of about 120,084 (296)

SHAT: A Novel Asynchronous Training Algorithm That Provides Fast Model Convergence in Distributed Deep Learning

Applied Sciences, 2021
The recent unprecedented success of deep learning (DL) in various fields is underlied by its use of large-scale data and models. Training a large-scale deep neural network (DNN) model with large-scale data, however, is time-consuming.
Yunyong Ko, Sang-Wook Kim
doaj   +1 more source

Hierarchical Heterogeneous Cluster Systems for Scalable Distributed Deep Learning [PDF]

, 2023
Distributed deep learning framework should aim at high efficiency of training and inference ofdistributed exascale deep learning algorithms. There are three major challenges in this endeavor: scalability, adaptivity and efficiency.
Wang, Yibo
core  

Training performance when batch normalization is applied in a distributed deep learning environment.

, 2023
Training performance when batch normalization is applied in a distributed deep learning environment.
Se Young Chun (17327871), Byung Hyun Lee (17327868), Jaehwan Lee (2235370), Hyeonseong Choi (17327865)   +3 more
core   +1 more source

Distributed deep learning training using silicon photonic switched architectures

APL Photonics, 2022
The scaling trends of deep learning models and distributed training workloads are challenging network capacities in today’s datacenters and high-performance computing (HPC) systems.
Ziyi Zhu, Min Yee Teh, Zhenguo Wu, Madeleine Strom Glick, Shijia Yan, Maarten Hattink, Keren Bergman   +6 more
doaj   +1 more source

Federated Learning via Augmented Knowledge Distillation for Heterogenous Deep Human Activity Recognition Systems

Sensors, 2022
Deep learning-based Human Activity Recognition (HAR) systems received a lot of interest for health monitoring and activity tracking on wearable devices.
Gad Gad, Zubair Fadlullah
doaj   +1 more source

Validation performance when batch normalization is applied in a distributed deep learning environment.

, 2023
Validation performance when batch normalization is applied in a distributed deep learning environment.
Se Young Chun (17327871), Byung Hyun Lee (17327868), Jaehwan Lee (2235370), Hyeonseong Choi (17327865)   +3 more
core   +1 more source

Unsupervised Deep Learning for Distributed Service Function Chain Embedding

IEEE Access, 2023
Network Function Virtualization (NFV) has paved the way for the migration of Virtual Network Functions (VNFs) into multi-tenant datacenters, lowering the barrier for the introduction of new processing functionality into the network.
Panteleimon Rodis, Panagiotis Papadimitriou   +1 more
doaj   +1 more source

Near-Optimal Sparse Allreduce for Distributed Deep Learning - PPoPP'2022 Artifact

, 2021
Artifact for the paper "Near-Optimal Sparse Allreduce for Distributed Deep Learning", published in PPoPP ...
Torsten Hoefler, Shigang Li
core   +1 more source

Soft Memory Box: A Virtual Shared Memory Framework for Fast Deep Neural Network Training in Distributed High Performance Computing

IEEE Access, 2018
Deep learning is one of the major promising machine learning methodologies. Deep learning is widely used in various application domains, e.g., image recognition, voice recognition, and natural language processing.
Shinyoung Ahn, Joongheon Kim, Eunji Lim, Sungwon Kang   +3 more
doaj   +1 more source

Institutionally Distributed Deep Learning Networks

CoRR, 2017
Deep learning has become a promising approach for automated medical diagnoses. When medical data samples are limited, collaboration among multiple institutions is necessary to achieve high algorithm performance. However, sharing patient data often has limitations due to technical, legal, or ethical concerns. In such cases, sharing a deep learning model
Ken Chang, Niranjan Balachandar, Carson K. Lam, Darvin Yi, James M. Brown 0001, Andrew Beers, Bruce R. Rosen, Daniel L. Rubin, Jayashree Kalpathy-Cramer   +8 more
openaire   +2 more sources