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Automatic distributed deep learning using resource-constrained edge devices

Author
Gutierrez-Torre, A.; Bahadori, K.; Baig, S.; Iqbal, W.; Vardanega, T.; Berral, J.; Carrera, D.
Type of activity
Journal article
Journal
IEEE internet of things journal
Date of publication
2021
First page
1
Last page
11
DOI
10.1109/JIOT.2021.3098973
Project funding
Models de Programacio i Entorns d'eXecució PARal.lels
UPC-High Performance Computing VIII
Repository
http://hdl.handle.net/2117/352129 Open in new window
URL
https://ieeexplore.ieee.org/document/9492272 Open in new window
Abstract
Processing data generated at high volume and speed from the Internet of Things, smart cities, domotic, intelligent surveillance, and e-healthcare systems require efficient data processing and analytics services at the Edge to reduce the latency and response time of the applications. The Fog Computing Edge infrastructure consists of devices with limited computing, memory, and bandwidth resources, which challenge the construction of predictive analytics solutions that require resource-intensive ta...
Citation
Gutiérrez-Torre, A. [et al.]. Automatic distributed deep learning using resource-constrained edge devices. "IEEE internet of things journal", 2021, p. 1-11.
Keywords
Analytics, Big data, Cloud computing, Edge computing, Fog computing, Internet of things (IoT), Resource management
Group of research
CAP - High Performace Computing Group

Participants

  • Gutiérrez Torre, Alberto  (author)
  • Bahadori, Kiyana  (author)
  • Baig, Shuja-ur-rehman  (author)
  • Iqbal, Waheed  (author)
  • Vardanega, Tullio  (author)
  • Berral Garcia, Josep Lluis  (author)
  • Carrera Perez, David  (author)

Attachments