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© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

The residue number system (RNS) is widely used in different areas due to the efficiency of modular addition and multiplication operations. However, non-modular operations, such as sign and division operations, are computationally complex. A fractional representation based on the Chinese remainder theorem is widely used. In some cases, this method gives an incorrect result associated with round-off calculation errors. In this paper, we optimize the division operation in RNS using the Akushsky core function without critical cores. We show that the proposed method reduces the size of the operands by half and does not require additional restrictions on the divisor as in the division algorithm in RNS based on the approximate method.

Details

Title
Improved Modular Division Implementation with the Akushsky Core Function
Author
Babenko, Mikhail 1   VIAFID ORCID Logo  ; Tchernykh, Andrei 2   VIAFID ORCID Logo  ; Kuchukov, Viktor 3 

 North-Caucasus Center for Mathematical Research, North-Caucasus Federal University, 355017 Stavropol, Russia; [email protected] (M.B.); [email protected] (V.K.); Institute for System Programming of the Russian Academy of Sciences, 109004 Moscow, Russia 
 Institute for System Programming of the Russian Academy of Sciences, 109004 Moscow, Russia; CICESE Research Center, Ensenada 22860, Mexico; School of Electrical Engineering and Computer Science, South Ural State University, 454080 Chelyabinsk, Russia 
 North-Caucasus Center for Mathematical Research, North-Caucasus Federal University, 355017 Stavropol, Russia; [email protected] (M.B.); [email protected] (V.K.) 
First page
9
Publication year
2022
Publication date
2022
Publisher
MDPI AG
e-ISSN
20793197
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2621279284
Copyright
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.