… object classification as the primary vision workload, which is in contrast to our envisioned
scenario here: mobile vision applications piggybacked on local GPU servers using a variety of …

… In the traditional content delivery network, only the data is cached at the edge servers. This
is based on the fact that the content provider provides the data on the Internet, which is true …

… edge cloud consists of multiple edge servers within radio access networks, eg, eNodeBs, that
are available for hosting computing tasks … and well-provisioned resources, edge clouds are …

… distinctive characteristics of edge landscape and IoT … vision for bringing Serverless to the
Edge Computing. It also issues major challenges for Serverless to be met before entering Edge …

… high-resolution mobile deep vision offloading in heterogeneous client and edge server
environment, by distributing the computation to available edge servers adaptively. Elf adopts …

… To alleviate such bottlenecks, we propose to utilize edge computing by equipping edge …
traffic monitoring tasks, congestion detection, and speed detection, and propose a two-tier edge …

… higher accuracy on many computer vision tasks than classic machine learning algorithms. …
edge services and the robot can offload real-time video analysis tasks to the EdgeEye server …

… In this work, we study the case where computationally heavy deep learning based vision
tasks are offloaded to an edge server over a 5G NR system. Our used 5G NR system’s overview …

… to handle a particular computation intensive task that is executed in the edge server. It may
be used in artificial intelligence, artificial vision and object tracking, or computational decision …

… We implement the edge-side modules of EdgeDuet on a CentOS 7.0 server. It is equipped
with two 8-core Intel Xeon CPU E5-2560 v4 CPUs, two GTX 2080ti GPUs and 256GB memory…