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 …

Big data powered Deep Learning (DL) and its applications have blossomed in recent years,
fueled by three technological trends: a large amount of digitized data openly accessible, a …

Deep learning (DL) has demonstrated great performance in various applications on
powerful computers and servers. Recently, with the advancement of more powerful mobile …

With increasing number of open-source deep learning (DL) software tools made available,
benchmarking DL software frameworks and systems is in high demand. This paper presents …

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

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 …

Modern Deep Learning systems heavily rely on distributed training over high-performance
accelerator (eg, TPU, GPU)-based hardware platforms. Examples today include Google's …

Distributed edge intelligence is a disruptive research area that enables the execution of
machine learning and deep learning (ML/DL) algorithms close to where data are generated …

Distributed deep neural network (DDNN) training constitutes an increasingly important
workload that frequently runs in the cloud. Larger DNN models and faster compute engines …

Large deep neural network models have recently demonstrated state-of-the-art accuracy on
hard visual recognition tasks. Unfortunately such models are extremely time consuming to …