… to be exchanged between edge devices and possibly the … major trends of deep learning
and edge computing, in particular … exist on deep learning [7] as well as edge computing [8], [9] …

… be integrated into edge computing frameworks to build intelligent edge for dynamic, adaptive
… edge computing frameworks and networks to better serve Edge DL, as “Edge computing for …

… deep learning technology for IoT and edge computing. Then we discuss the deep learning
services for IoT in the edge computing … scheduling IoT deep learning tasks in edge computing. …

… on the confluence of edge computing and deep learning, and … of leveraging deep learning
to empower the edge computing … overview of edge computing and deep learning on concepts, …

… Edge computing is revolutionizing the way we live, work, and interact with the world. With …
in deep learning, it is expected that in the foreseeable future, majority of the edge devices will …

… computing environment. We establish a multi-layer edge computing architecture and a …
The DIVS system can migrate computing workloads from the network center to network edges to …

… In this work, we consider a small cell-based mobile edge computing system, which is also
known as small cell cloud. The basic idea is to enhance small cell base stations (eg, pico, …

… To this end, we propose an edge computing-based data processing approach for the RMCS
framework, where the raw data such as images or video clips are processed at edge nodes. …

… ABSTRACT Mobile edge computing (MEC) has shown tremendous potential as a means for
… -efficient deep learning based offloading scheme (EEDOS) to train a deep learning based …

… Is a CMOS-based design with traditional memory hierarchy the optimal solution for DNN
processing, or should we adopt in-memory computing for deep learning at the edge? …