Modern deep learning models have been exploited in various domains, including computer
vision (CV), natural language processing (NLP), search and recommendation. In practical AI …

Training deep learning models on mobile devices recently becomes possible, because of
increasing computation power on mobile hardware and the advantages of enhancing user …

This work examines the performance of leading-edge systems designed for machine
learning computing, including the NVIDIA DGX-2, Amazon Web Services (AWS) P3, IBM …

Since emerging edge applications such as Internet of Things (IoT) analytics and augmented
reality have tight latency constraints, hardware AI accelerators have been recently proposed …

Deep learning has become widely used in complex AI applications. Yet, training a deep
neural network (DNNs) model requires a considerable amount of calculations, long running …

Nowdays, it is prevalent to train deep learning (DL) models in cloud-native platforms that
actively leverage containerization and orchestration technologies for high elasticity, low and …

The recent explosion in the popularity of Deep Learning (DL) is due to a combination of
improved algorithms, access to large datasets and increased computational power. This had …

Big Data has fueled the wide deployment of Deep Learning (DL) in many fields, such as
image classification, voice recognition and NLP. The growing number of open source DL …

Deep Learning (DL) algorithms have become ubiquitous in data analytics. As a result, major
computing vendors–including NVIDIA, Intel, AMD, and IBM–have architectural road maps …

Training deep learning models is compute-intensive and there is an industry-wide trend
towards hardware specialization to improve performance. To systematically benchmark …