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  5. Low-Complexity Concurrent Error Detection for Convolution with Fast Fourier Transforms
 
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Low-Complexity Concurrent Error Detection for Convolution with Fast Fourier Transforms

Author(s)
Bleakley, Chris J.  
Reviriego, P.  
Maestro, J.A.  
Uri
http://hdl.handle.net/10197/7130
Date Issued
2011-06
Date Available
2015-09-28T15:36:45Z
Abstract
In this paper, a novel low-complexity Concurrent Error Detection (CED) technique for Fast Fourier Transform-based convolution is proposed. The technique is based on checking the equivalence of the results of time and frequency domain calculations of the first sample of the circular convolution of the two convolution input blocks and of two consecutive output blocks. The approach provides low computational complexity since it re-uses the results of the convolution computation for CED checking. Hence, the number of extra calculations needed purely for CED is significantly reduced. When compared with a conventional Sum Of Squares - Dual Modular Redundancy technique, the proposal provides similar error coverage for isolated soft errors at significantly reduced computational complexity. For an input sequence consisting of complex numbers, the proposal reduces the number of real multiplications required for CED in adaptive and fixed filters by 60% and 45%, respectively. For input sequences consisting of real numbers, the reductions are 66% and 54%, respectively.
Type of Material
Journal Article
Publisher
Elsevier
Journal
Microelectronics Reliability
Volume
51
Issue
6
Start Page
1152
End Page
1156
Copyright (Published Version)
2011 Elsevier
Subjects

Convolution

FFT

Soft Errors

DOI
10.1016/j.microrel.2011.02.010
Language
English
Status of Item
Peer reviewed
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
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Low_Complexity_Concurrent_Error_Detection_for_Fast_Fourier_Transform_based_Convolution.pdf

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Owning collection
Computer Science Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
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