Paper 2017/005

High-speed Hardware Implementations of Point Multiplication for Binary Edwards and Generalized Hessian Curves

Bahram Rashidi, Reza Rezaeian Farashahi, and Sayed Masoud Sayedi

Abstract

In this paper high-speed hardware architectures of point multiplication based on Montgomery ladder algorithm for binary Edwards and generalized Hessian curves in Gaussian normal basis are presented. Computations of the point addition and point doubling in the proposed architecture are concurrently performed by pipelined digit-serial finite field multipliers. The multipliers in parallel form are scheduled for lower number of clock cycles. The structure of proposed digit-serial Gaussian normal basis multiplier is constructed based on regular and low-cost modules of exponentiation by powers of two and multiplication by normal elements. Therefore, the structures are area efficient and have low critical path delay. Implementation results of the proposed architectures on Virtex-5 XC5VLX110 FPGA show that then execution time of the point multiplication for binary Edwards and generalized Hessian curves over GF(2163) and GF(2233) are 8.62µs and 11.03µs respectively. The proposed architectures have high-performance and high-speed compared to other works.

Metadata
Available format(s)
PDF
Category
Implementation
Publication info
Preprint. MINOR revision.
Contact author(s)
b_rashidi86 @ yahoo com
History
2017-01-11: received
Short URL
https://ia.cr/2017/005
License
Creative Commons Attribution
CC BY

BibTeX

@misc{cryptoeprint:2017/005,
      author = {Bahram Rashidi and Reza Rezaeian Farashahi and Sayed Masoud Sayedi},
      title = {High-speed Hardware Implementations of Point Multiplication for Binary Edwards and Generalized Hessian Curves},
      howpublished = {Cryptology ePrint Archive, Paper 2017/005},
      year = {2017},
      note = {\url{https://eprint.iacr.org/2017/005}},
      url = {https://eprint.iacr.org/2017/005}
}
Note: In order to protect the privacy of readers, eprint.iacr.org does not use cookies or embedded third party content.