2013
DOI: 10.1007/978-3-642-40349-1_14
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Improving Modular Inversion in RNS Using the Plus-Minus Method

Abstract: Abstract. The paper describes a new RNS modular inversion algorithm based on the extended Euclidean algorithm and the plus-minus trick. In our algorithm, comparisons over large RNS values are replaced by cheap computations modulo 4. Comparisons to an RNS version based on Fermat's little theorem were carried out. The number of elementary modular operations is significantly reduced: a factor 12 to 26 for multiplications and 6 to 21 for additions. Virtex 5 FPGAs implementations show that for a similar area, our p… Show more

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Cited by 13 publications
(9 citation statements)
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“…In this section, we first present the algorithm and the classical ALU used to compute the reduction inside the Rower. To our knowledge, it is the only ALU used with the RNS Cox-Rower architecture [8,5,3,13,2].…”
Section: A New Cox-rower Architecturementioning
confidence: 99%
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“…In this section, we first present the algorithm and the classical ALU used to compute the reduction inside the Rower. To our knowledge, it is the only ALU used with the RNS Cox-Rower architecture [8,5,3,13,2].…”
Section: A New Cox-rower Architecturementioning
confidence: 99%
“…It is also possible to reduce the last addition during the computation of the multiplications, if the adder/reducer block are not the critical path of the design compared to the multipliers. Such implementation gives good results for efficient implementation and computation for F p /RSA and ECC [6,8,5,3,13,2]. Figure 1 presents the ALU of the Rower unit introduced by Guillermin [5].…”
Section: Classical Rower Unitmentioning
confidence: 99%
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“…Residue Number Systems (RNS) have been proven to be a good candidate for achieving fast computation in finite fields [5], [6], [9], [15], [18], which is a critical issue for limiting latency of public key cryptography. In practice, most of the competitive hardware implementations rely on the so-called Cox-Rower architecture, introduced by Kawamura et al [15], which is designed to fit with natural properties of RNS.…”
Section: Introductionmentioning
confidence: 99%