Optimized CNN Architectures Benchmarking in Hardware-Constrained Edge Devices in IoT Environments-Reference-Cited by-同舟云学术

Optimized CNN Architectures Benchmarking in Hardware-Constrained Edge Devices in IoT Environments

Published:2024-06-01 Issue:11 Volume:11 Page:20357-20366
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Rosero-Montalvo Paul D.¹^ORCID,Tözün Pınar¹^ORCID,Hernandez Wilmar²^ORCID

Affiliation:

1. Computer Science Department, The IT University of Copenhagen, Kobenhavn, Denmark

2. Carrera de Ingenieria Electronica y Automatizacion, Facultad de Ingenieria y Ciencias Aplicadas, Universidad de Las Americas, Quito, Ecuador

Funder

Universidad de Las Americas (UDLA), Quito, Ecuador

Corporacion Ecuatoriana para el Desarrollo de la Investigacion y la Academia (CEDIA), Ecuador

Independent Research Fund Denmark’s (Danmarks Frie Forskningsfond; DFF) Inge Lehmann Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6488907/10536937/10444008.pdf?arnumber=10444008

Reference41 articles.

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2. Edge intelligence: Paving the last mile of artificial intelligence with edge computing;Zhou;Proc. IEEE,2019

3. Edge intelligence: The confluence of edge computing and artificial intelligence;Deng;IEEE Internet Things J.,2020

4. Edge of things: The big picture on the integration of edge, IoT and the cloud in a distributed computing environment;El-Sayed;IEEE Access,2018

5. Knowledge Transfer for On-Device Deep Reinforcement Learning in Resource Constrained Edge Computing Systems

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