Exploring Energy Efficient Architectures for RLWE Lattice-Based Cryptography
| Autor: | Hamid Nejatollahi, Rosario Cammarota, Sina Shahhosseini, Nikil Dutt |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Polynomial
Speedup Computer science business.industry 020206 networking & telecommunications Cryptography 02 engineering and technology Theoretical Computer Science Convolution Computational science Hardware and Architecture Control and Systems Engineering Modeling and Simulation Signal Processing 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Multiplier (economics) Degree of a polynomial Lattice-based cryptography business Field-programmable gate array Information Systems |
| Zdroj: | Journal of Signal Processing Systems. 93:1139-1148 |
| ISSN: | 1939-8115 1939-8018 |
| DOI: | 10.1007/s11265-020-01627-x |
| Popis: | Quantum computers are imminent threat to secure signal processing because they can break the contemporary public-key cryptography schemes in polynomial time. Ring learning with error (RLWE) lattice-based cryptography (LBC) is considered as the most versatile and efficient family of post-quantum cryptography (PQC). Polynomial multiplication is the most compute-intensive routine in the RLWE schemes. Convolutions and Number Theoretic Transform (NTT) are two common methods to perform the polynomial multiplication. In this paper, we explore the energy efficiency of different polynomial multipliers, NTT-based and convolution-based, on GPU and FPGA. When synthesized on a Zynq UltraScale+ FPGA, our NTT-based and convolution-based designs achieve on average 5.1x and 22.5x speedup over state-of-the-art. Our convolution-based design, on a Zynq UltraScale+ FPGA, can generate more than 2x signatures per second by CRYSTALS-Dilithium. The designed NTT-based multiplier on NVIDIA Jetson TX2 is 1.2x and 2x faster than our baseline NTT-based multiplier on FPGA for polynomial degrees of 512 and 1024, respectively. Our explorations and guidelines can help designers choose proper implementations to realize quantum-resistant signal processing. |
| Databáze: | OpenAIRE |
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