On the challenge of training small scale neural networks on large scale computing systems
- We present a novel approach of distributing small-to mid-scale neural networks onto modern parallel architectures. In this context we discuss the induced challenges and possible solutions. We provide a detailed theoretical analysis with respect to space and time complexities and reinforce our computation model with evaluations which show a performance gain over state of the art approaches.
| Author: | Darius Malysiak, Matthias Grimm, Uwe Handmann |
|---|---|
| URL: | https://ieeexplore.ieee.org/document/7382935 |
| DOI: | https://doi.org/10.1109/CINTI.2015.7382935 |
| ISBN: | 978-1-4673-8520-6 |
| Parent Title (English): | 16th IEEE International Symposium on Computational Intelligence and Informatics (CINTI) |
| Document Type: | Conference Proceeding |
| Language: | English |
| Year of Completion: | 2015 |
| Contributing Corporation: | IEEE |
| Release Date: | 2019/07/03 |
| Page Number: | 12 |
| First Page: | 273 |
| Last Page: | 284 |
| Institutes: | Fachbereich 1 - Institut Informatik |
| DDC class: | 000 Allgemeines, Informatik, Informationswissenschaft / 004 Informatik |
| Licence (German): |  No Creative Commons |
