Deep learning frameworks have been widely deployed on GPU servers for deep learning
applications in both academia and industry. In training deep neural networks (DNNs), there …

Recent years saw an increasing success in the application of deep learning methods across
various domains and for tackling different problems, ranging from image recognition and …

Because training a deep neural network (DNN) takes arduous amounts of time and
computation, often researchers expedite the training process via distributed parallel training …

Deep learning models can take weeks to train on a single GPU-equipped machine,
necessitating scaling out DL training to a GPU-cluster. However, current distributed DL …

Deep Learning models need massive amounts compute powers and tend to improve
performance running on special purpose processors accelerators designed to speed up …

As one of the most important deep learning models, convolutional neural networks (CNNs)
have achieved great successes in a number of applications such as image classification …

The recent surge of Deep Learning (DL) models and applications can be attributed to the
rise in computational resources, availability of large-scale datasets, and accessible DL …

High performance multi-GPU systems are widely used to accelerate training of deep neural
networks (DNNs) by exploiting the inherently massive parallel nature of the training process …

Deep learning has been shown as a successful machine learning method for a variety of
tasks, and its popularity results in numerous open-source deep learning software tools …

We evaluate one of the state-of-the-art GPU-accelerated edge devices in this paper. We
perform a set of deep learning benchmarks on the device to measure its performance. By …