… Deep Learning With Edge Computing: A Review … of applications where deep learning is
used at the network edge. … We then discuss training deep learning models on 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 …

… 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 …

… , 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 …

… based tensor library for deep learning research. As suggested in [17], [18], we create multiple
virtual machines (VMs) with constrained resources to emulate our edge devices. All VMs …

… a new paradigm of intelligence called edge intelligence (EI) [… resources available at end
devices, edge servers, and cloud … of cloud servers, edge servers, and end devices. DL training …

… server and the cloud to unleash the full power of the deep learning networks therein. To … to
partition deep learning networks in mobile applications to hybridly run on the device, the edge …

… 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 …

… 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 …

… directions for edge computing deep learning algorithms. … proposed to deploy deep learning
on the edge are identified and … devices to execute deep learning models on the edge; …