Artificial intelligence has made remarkable progress in handling complex tasks, thanks to
advances in hardware acceleration and machine learning algorithms. However, to acquire …

As the popularity of deep learning in industry rapidly grows, efficient training of deep neural
networks (DNNs) becomes important. To train a DNN with a large amount of data, distributed …

To speed up the training of massive deep neural network (DNN) models, distributed training
has been widely studied. In general, a centralized training, a type of distributed training …

The interest and demand for training deep neural networks have been experiencing rapid
growth, spanning a wide range of applications in both academia and industry. However …

Distributed machine learning (ML) can bring more computational resources to bear than
single-machine learning, thus enabling reductions in training time. Distributed learning …

Deep learning have been widely used in various fields and has worked very well as a major
role. While the gradual penetration into various fields, data quantity of each applications is …

Synchronous strategies with data parallelism, such as the Synchronous StochasticGradient
Descent (S-SGD) and the model averaging methods, are widely utilizedin distributed …

Driven by big data, neural networks evolve more complex and the computing capacity of a
single machine is often difficult to meet the demand. Distributed deep learning technology …

With growing scale of the data volume and neural network size, we have come into the era
of distributed deep learning. High-performance training and inference on distributed …

Deep neural networks and deep learning are becoming important and popular techniques in
modern services and applications. The training of these networks is computationally …