Deep neural networks (DNNs) have gained tremendous attractions as compelling solutions
for applications such as image classification, object detection, speech recognition, and so …

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 …

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 …

Training real-world Deep Neural Networks (DNNs) can take an eon (ie, weeks or months)
without leveraging distributed systems. Even distributed training takes inordinate time, of …

It is important to scale out deep neural network (DNN) training for reducing model training
time. The high communication overhead is one of the major performance bottlenecks for …

It is important to scale out deep neural network (DNN) training for reducing model training
time. The high communication overhead is one of the major performance bottlenecks for …

The Convolutional Neural Network (CNN) model, often used for image classification,
requires significant training time to obtain high accuracy. To this end, distributed training is …

Deep Learning (DL) training platforms are built by interconnecting multiple DL accelerators
(eg, GPU/TPU) via fast, customized interconnects with 100s of gigabytes (GBs) of bandwidth …

It becomes an increasingly popular trend for deep neural networks with large-scale datasets
to be trained in a distributed manner in the cloud. However, widely known as resource …

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