Efficient Parallel Hardware Architecture for GF(p) Elliptic Curve Point Multiplication on FPGA

ABSTRACT Elliptic curve point multiplication is a critical component in elliptic curve cryptography, serving as a key computational module for fast and secure encryption. To enhance computational performance, parallelism and pipelining are commonly employed techniques in hardware design. However, the stringent computational dependencies in elliptic curve cryptography and the complex combinational logic required for large integer multiplication present significant challenges for area‐efficient hardware design, leading to performance bottlenecks. This paper aims to improve system frequency by employing multilevel pipeline partitioning to optimize the critical path. We implement an efficient, low‐latency, high‐radix modular multiplication unit by combining parallel multiplication with deep pipelining. Building on this high‐frequency, high‐throughput modular multiplication unit, we propose a high‐performance ECPM architecture. Multistage pipelines are inserted into each unit of the ECPM to maintain high‐frequency operation across the entire system. Additionally, by analyzing and refining the combined point quadruple‐point addition computation flow and data dependencies, we introduce a no‐idle parallel modular multiplier architecture, which improves cycle efficiency per computation iteration by 9 % . Experimental results on the Virtex‐7 FPGA platform demonstrate that, compared with related works, the proposed ECPM architecture achieves a 30 % improvement in Area Time.

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Publication Details

Journal
International Journal of Circuit Theory and Applications
Published
2025-03-05
DOI
https://doi.org/10.1002/cta.4495
Primary Topic
Cryptography and Residue Arithmetic
Type
article
Field-Weighted Citation Impact
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article

Efficient Parallel Hardware Architecture for GF(p) Elliptic Curve Point Multiplication on FPGA

Guanzhong Tian, Ruoyu Wu, Xiangyu Li, Zhaofeng Chen et al.
International Journal of Circuit Theory and Applications
Cryptography and Residue Arithmetic
article

Efficient Parallel Hardware Architecture for GF(p) Elliptic Curve Point Multiplication on FPGA

Guanzhong Tian, Ruoyu Wu, Xiangyu Li, Zhaofeng Chen, Binchao Yu
article en

Abstract

ABSTRACT Elliptic curve point multiplication is a critical component in elliptic curve cryptography, serving as a key computational module for fast and secure encryption. To enhance computational performance, parallelism and pipelining are commonly employed techniques in hardware design. However, the stringent computational dependencies in elliptic curve cryptography and the complex combinational logic required for large integer multiplication present significant challenges for area‐efficient hardware design, leading to performance bottlenecks. This paper aims to improve system frequency by employing multilevel pipeline partitioning to optimize the critical path. We implement an efficient, low‐latency, high‐radix modular multiplication unit by combining parallel multiplication with deep pipelining. Building on this high‐frequency, high‐throughput modular multiplication unit, we propose a high‐performance ECPM architecture. Multistage pipelines are inserted into each unit of the ECPM to maintain high‐frequency operation across the entire system. Additionally, by analyzing and refining the combined point quadruple‐point addition computation flow and data dependencies, we introduce a no‐idle parallel modular multiplier architecture, which improves cycle efficiency per computation iteration by 9 % . Experimental results on the Virtex‐7 FPGA platform demonstrate that, compared with related works, the proposed ECPM architecture achieves a 30 % improvement in Area Time.

International Journal of Circuit Theory and ApplicationsVol. 53(10)
Zhejiang University (CN)
Openalex Percentile: Top 95%
Cryptography and Residue Arithmetic
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Efficient Parallel Hardware Architecture for GF(p) Elliptic Curve Point Multiplication on FPGA — Guanzhong Tian, Ruoyu Wu, et al. · International Journal of Circuit Theory and Applications (2025) | TGRS Research Map | TGRS