Distributed deep learning systems (DDLS) train deep neural network models by utilizing the
distributed resources of a cluster. Developers of DDLS are required to make many decisions …

Big deep neural network (DNN) models trained on large amounts of data have recently
achieved the best accuracy on hard tasks, such as image and speech recognition. Training …

Deep learning frameworks such as TensorFlow and PyTorch provide a productive interface
for expressing and training a deep neural network (DNN) model on a single device or using …

The prediction of the resource consumption for the distributed training of deep learning
models is of paramount importance, as it can inform a priori users how long their training …

Deep learning with a large number of parametersrequires distributed training, where model
accuracy and runtimeare two important factors to be considered. However, there hasbeen …

Decentralized training of deep learning models enables on-device learning over networks,
as well as efficient scaling to large compute clusters. Experiments in earlier works reveal …

Deep Learning (DL) has had an immense success in the recent past, leading to state-of-the-
art results in various domains, such as image recognition and natural language processing …

The demand for artificial intelligence has grown significantly over the past decade, and this
growth has been fueled by advances in machine learning techniques and the ability to …

We propose an efficient protocol for decentralized training of deep neural networks from
distributed data sources. The proposed protocol allows to handle different phases of model …

Distributed systems for the training of deep neural networks (DNNs) with large amounts of
data have vastly improved the accuracy of machine learning models for image and speech …