Deploying deep learning (DL) on mobile devices has been a notable trend in recent years.
To support fast inference of on-device DL, DL libraries play a critical role as algorithms and …

Deploying deep learning (DL) on mobile devices has been a notable trend in recent years.
To support fast inference of on-device DL, DL libraries play a critical role as algorithms and …

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

Deploying deep learning (DL) models on mobile devices draws more and more attention
recently. However, designing an efficient inference engine on devices is under the great …

Radical progress in the field of deep learning (DL) has led to unprecedented accuracy in
diverse inference tasks. As such, deploying DL models across mobile platforms is vital to …

Although running deep-learning (DL) algorithms is challenging due to resource constraints
on mobile and wearable devices, they provide performance improvements compared to …

The staggering innovations and emergence of numerous deep learning (DL) applications
have forced researchers to reconsider hardware architecture to accommodate fast and …

When building Deep Learning (DL) models, data scientists and software engineers manage
the trade-off between their accuracy, or any other suitable success criteria, and their …

Recent deep learning (DL) applications are mostly built on top of DL libraries. The quality
assurance of these libraries is critical to the dependable deployment of DL applications …

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 …