Record link: 
http://hdl.handle.net/20.500.12708/142187
-
Title: 
enpheeph: A Fault Injection Framework for Spiking and Compressed Deep Neural Networks
en
Citation: 
Colucci, A., Steininger, A., & Shafique, M. (2022). enpheeph: A Fault Injection Framework for Spiking and Compressed Deep Neural Networks. In Proceedings 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 5155–5162). https://doi.org/10.1109/IROS47612.2022.9982181
-
Publisher DOI: 
10.1109/IROS47612.2022.9982181
-
Publication Type: 
Inproceedings - Full-Paper Contribution
en
Language: 
English
-
Authors: 
Colucci, Alessio 
Steininger, Andreas 
Shafique, Muhammad 
-
Organisational Unit: 
E191-02 - Forschungsbereich Embedded Computing Systems 
-
Published in: 
Proceedings 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 
-
ISBN: 
978-1-6654-7927-1
-
Date (published): 
2022
-
Event name: 
2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 
en
Event date: 
23-Oct-2022 - 27-Oct-2022
-
Event place: 
Kyoto, Japan
-
Number of Pages: 
8
-
Keywords: 
fault injection; neural networks; quantization; sparse tensor; spiking neural networks
en
Research Areas: 
Computer Engineering and Software-Intensive Systems: 100%
-
Science Branch: 
1020 - Informatik: 100%
-
Appears in Collections:
Conference Paper

Show full item record

Page view(s)

115
checked on Nov 21, 2023

Download(s)

3
checked on Nov 21, 2023

Google ScholarTM

Check