Date of Award
2021
Publisher
North Dakota State University
Document Type
Thesis
Degree Awarded
Master of Science (MS)
Department
Computer Science
College
Engineering
Faculty Advisor
Ludwig, Simone
Description
Computer vision based applications are commonly utilized in embedded systems. The demand for higher accuracy leads to increased complexity of convolutional neural networks (CNN) and hence, larger storage requirement for saving pre-trained networks. Even the smallest data corruption in the storage units of the embedded systems can result in drastic failures due to the propagation of the errors. This thesis proposes a new algorithm for the recovery of the data in case of single event upset (SEU) error. An association rule mining based algorithm is used to find the probability of corruption in each of the bits. The recovery algorithm is tested on four different trained ResNet and the best recovery rate of 66% was found in the most complex scenario. However, for the special cases of corruption in the frequently repeated bits, the recovery rate was found to be perfect with 100% recovery rate.
Recommended Citation
Ramzanpour, Mohammadreza, "Development of an Algorithm for Detection and Recovery of Corruption in Convolutional Neural Networks Data Storage" (2021). Computer Science. 290.
https://digitalcommons.ndsu.edu/computer-sci/290
Rights
NDSU policy 190.6.2
Rights Link
https://www.ndsu.edu/fileadmin/policy/190.pdf
Handle Identifier
https://hdl.handle.net/10365/32622