Concurrent error detection in bit-serial normal basis multiplication over GF(2^m) using multiple parity prediction schemes
New bit-serial architectures with concurrent error detection capability are presented to detect erroneous outputs in bit-serial normal basis multipliers over GF(2^m) using single and multiple-parity prediction schemes. It is shown that different types of normal basis multipliers could be realized by...
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sg-ntu-dr.10356-943462020-05-28T07:17:24Z Concurrent error detection in bit-serial normal basis multiplication over GF(2^m) using multiple parity prediction schemes Lee, Chiou Yng Meher, Pramod Kumar Patra, Jagdish Chandra School of Computer Engineering DRNTU::Engineering::Computer science and engineering::Computer systems organization::Computer-communication networks New bit-serial architectures with concurrent error detection capability are presented to detect erroneous outputs in bit-serial normal basis multipliers over GF(2^m) using single and multiple-parity prediction schemes. It is shown that different types of normal basis multipliers could be realized by similar architectures. The proposed architectures can detect errors with nearly 100% probability. Accepted version 2011-09-29T06:13:25Z 2019-12-06T18:54:36Z 2011-09-29T06:13:25Z 2019-12-06T18:54:36Z 2010 2010 Journal Article Lee, C. Y., Meher, P. K., & Patra, J. C. (2010). Concurrent Error Detection in Bit-Serial Normal Basis Multiplication Over GF (2^m) Using Multiple Parity Prediction Schemes. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 18(8), 1234-1238. 1063-8210 https://hdl.handle.net/10356/94346 http://hdl.handle.net/10220/7120 10.1109/TVLSI.2009.2020593 141722 en IEEE transactions on very large scale integration (VLSI) systems © 2009 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [DOI: http://dx.doi.org/10.1109/TVLSI.2009.2020593]. 5 p. application/pdf |
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DRNTU::Engineering::Computer science and engineering::Computer systems organization::Computer-communication networks Lee, Chiou Yng Meher, Pramod Kumar Patra, Jagdish Chandra Concurrent error detection in bit-serial normal basis multiplication over GF(2^m) using multiple parity prediction schemes |
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New bit-serial architectures with concurrent error detection capability are presented to detect erroneous outputs in bit-serial normal basis multipliers over GF(2^m) using single and multiple-parity prediction schemes. It is shown that different types of normal basis multipliers could be realized by similar architectures. The proposed architectures can detect errors with nearly 100% probability. |
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School of Computer Engineering |
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School of Computer Engineering Lee, Chiou Yng Meher, Pramod Kumar Patra, Jagdish Chandra |
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Article |
author |
Lee, Chiou Yng Meher, Pramod Kumar Patra, Jagdish Chandra |
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Lee, Chiou Yng |
title |
Concurrent error detection in bit-serial normal basis multiplication over GF(2^m) using multiple parity prediction schemes |
title_short |
Concurrent error detection in bit-serial normal basis multiplication over GF(2^m) using multiple parity prediction schemes |
title_full |
Concurrent error detection in bit-serial normal basis multiplication over GF(2^m) using multiple parity prediction schemes |
title_fullStr |
Concurrent error detection in bit-serial normal basis multiplication over GF(2^m) using multiple parity prediction schemes |
title_full_unstemmed |
Concurrent error detection in bit-serial normal basis multiplication over GF(2^m) using multiple parity prediction schemes |
title_sort |
concurrent error detection in bit-serial normal basis multiplication over gf(2^m) using multiple parity prediction schemes |
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2011 |
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https://hdl.handle.net/10356/94346 http://hdl.handle.net/10220/7120 |
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1681058520680628224 |