E Li, Z Zhou, X Chen - … of the 2018 workshop on mobile edge …, 2018 - dl.acm.org
… 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 …
H Li, K Ota, M Dong - IEEE network, 2018 - ieeexplore.ieee.org
… Then we discuss the deeplearning services for IoT in the edge … of scheduling IoT deep learning tasks in edge computing. Then … a new deeplearning model for wearable IoT devices that …
… , and applications onto edgedevices 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 …
F Xue, W Fang, W Xu, Q Wang, X Ma… - 2020 IEEE 22nd …, 2020 - ieeexplore.ieee.org
… based tensor library for deeplearning research. As suggested in [17], [18], we create multiple virtual machines (VMs) with constrained resources to emulate our edgedevices. 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 deeplearning networks therein. To … to partition deeplearning networks in mobile applications to hybridly run on the device, the edge …
MP Véstias - Smart Systems Design, Applications, and Challenges, 2020 - igi-global.com
… of edgedevices to run machine learning inference… deeplearning: applications, deeplearning models, and computing platforms. The way deeplearning is being applied to edgedevices …
… deeplearning 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 deeplearning algorithms. … proposed to deploy deeplearning on the edge are identified and … devices to execute deeplearning models on the edge; …