Journal
2020 IEEE 24TH INTERNATIONAL CONFERENCE ON INTELLIGENT ENGINEERING SYSTEMS (INES 2020)
Volume -, Issue -, Pages 165-170Publisher
IEEE
DOI: 10.1109/ines49302.2020.9147123
Keywords
-
Funding
- European Novel EOSC services for Emerging Atmosphere, Underwater and Space Challenges (NEANIAS) project [863448]
- National Research, Development and Innovation Office (NKFIH) under OTKA [K 132838]
- Doctoral School of Applied Informatics and Applied Mathematics, Obuda University
Ask authors/readers for more resources
Deep neural networks and deep learning are becoming important and popular techniques in modern services and applications. The training of these networks is computationally intensive, because of the extreme number of trainable parameters and the large amount of training samples. In this brief overview, current solutions aiming to speed up this training process via parallel and distributed computation are introduced. The necessary components and strategies are described from the low-level communication protocols to the high-level frameworks for the distributed deep learning. The current implementations of the deep learning frameworks with distributed computational capabilities are compared and key parameters are identified to help design effective solutions.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available