The Case for Hierarchical Deep Learning Inference at the Network Edge

Autor: Al-Atat, Ghina, Fresa, Andrea, Behera, Adarsh Prasad, Moothedath, Vishnu Narayanan, Gross, James, Champati, Jaya Prakash

Resource-constrained Edge Devices (EDs), e.g., IoT sensors and microcontroller units, are expected to make intelligent decisions using Deep Learning (DL) inference at the edge of the network. Toward this end, there is a significant research effort in

Externí odkaz: http://arxiv.org/abs/2304.11763

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An Offloading Algorithm for Maximizing Inference Accuracy on Edge Device in an Edge Intelligence System

Autor: Fresa, Andrea, Champati, Jaya Prakash

Publikováno v: Proceedings of the International Conference on Modeling Analysis and Simulation of Wireless and Mobile Systems on International Conference on Modeling Analysis and Simulation of Wireless and Mobile Systems

With the emergence of edge computing, the problem of offloading jobs between an Edge Device (ED) and an Edge Server (ES) received significant attention in the past. Motivated by the fact that an increasing number of applications are using Machine Lea

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::611cd6972ef80f7eb1e22574dff8a377