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Resource-efficient models for edge devices
This chapter surveys the reasons and methods for building resource-efficient AI for the network edge, where devices operate with tight limits on computing, memory, energy, and bandwidth. First, it motivates the need for on-device intelligence by linking the rapid growth of edge deployments with requirements for low latency, privacy, and real-time ...Rehman, Mujeeb Ur +3 more
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Edge analytics on resource constrained devices
International Journal of Computational Science and Engineering, 2023Sean Savitz +2 more
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Distributing DNN training over IoT edge devices based on transfer learning
Neurocomputing, 2022Mehdi Kamal +2 more
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Wireless Distributed Edge Learning: How Many Edge Devices Do We Need?
IEEE Journal on Selected Areas in Communications, 2021Jaeyoung Song, Marios Kountouris
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Light-Edge: A Lightweight Authentication Protocol for IoT Devices in an Edge-Cloud Environment
IEEE Consumer Electronics Magazine, 2022Alireza Souri +2 more
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Efficient Acceleration of Deep Learning Inference on Resource-Constrained Edge Devices: A Review
Proceedings of the IEEE, 2023Syed Kamrul Islam +2 more
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Software as a Device in Edge Computing
Proceedings of the Annual Hawaii International Conference on System SciencesRadmila Juric +5 more
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