NDLI: Fault Models for Neural Hardware

Content Provider	IEEE Xplore Digital Library
Author	Singh, A.P. Chandra, P. Rai, C.S.
Copyright Year	2009
Abstract	Artificial Neural Networks are inherently fault tolerant. Fault tolerance property of artificial neural networks has been investigated with reference to the hardware model of artificial neural networks. In this paper, we propose a framework for the investigation of fault tolerance properties of a hardware model of artificial neural networks. The result obtained indicates that networks obtained by training them with the resilient back propagation algorithm are not fault tolerant: more experimentation is required before a definitive statement can be made for other training algorithms, like the adaptive learning rate algorithm, the conjugate gradient based training algorithms, etc.
Starting Page	7
Ending Page	12
File Size	356857
Page Count	6
File Format	PDF
ISBN	9781424448623
DOI	10.1109/VALID.2009.32
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-20
Publisher Place	Portugal
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural network hardware Application specific integrated circuits Fault tolerance Neurons Artificial neural networks Multilayer perceptrons Parallel processing Artificial neural network Circuit faults Biological neural networks Field programmable gate arrays Fault model
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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Fault Models for Neural Hardware