… training deep learning models on edge devices, … deep learning on edge devices is useful,
and what “real time” means for each of these applications. Other applications of deep learning …

… , and applications onto edge devices is considered to … learning methods, namely, Deep
Learning (DL) and offer a short survey on the recent approaches used to map DL onto the edge …

… Hence, new resource and energy-oriented deep learning models … directions for edge
computing deep learning algorithms. … In this context, edge devices include end devices and edge …

… Then we discuss the deep learning services for IoT in the edge … of scheduling IoT deep
learning tasks in edge computing. Then … a new deep learning model for wearable IoT devices that …

… of edge devices to run machine learning inference… deep learning: applications, deep learning
models, and computing platforms. The way deep learning is being applied to edge devices …

… To answer the above question in the positive, in this paper we proposed Edgent, a deep
learning model co-inference framework with device-edge synergy. Towards low-latency …

… The success of deep learning comes at the cost of very high … (IoT) edge nodes typically offload
deep learning tasks to … issues is to perform deep learning inference at the edge. However, …

… We describe the existing computing devices for deep learning and … devices for deep
learning on edge, what has been researched, and what should we expect from future devices…

… Our method also helps combat COVID-19 by providing affordable deep learning solutions
detecting face masks and people gathering on edge devices without sacrificing privacy. …

… deep learning training tasks on edgedevices. Because of upload constraints of devices and
… asynchronous SGD is a natural solution to perform learning tasks; yet we highlighted that the …