KyberMat: Efficient Accelerator for Matrix-Vector Polynomial Multiplication in CRYSTALS-Kyber Scheme via NTT and Polyphase Decomposition

• URL: http://arxiv.org/abs/2310.04618v1
• Date: Fri, 6 Oct 2023 22:57:25 GMT
• Title: KyberMat: Efficient Accelerator for Matrix-Vector Polynomial Multiplication in CRYSTALS-Kyber Scheme via NTT and Polyphase Decomposition
• Authors: Weihang Tan, Yingjie Lao, Keshab K. Parhi,
• Abstract summary: CRYSTAL-Kyber (Kyber) is one of the post-quantum cryptography (PQC) key-encapsulation mechanism (KEM) schemes selected during the standardization process. This paper addresses optimization for Kyber architecture with respect to latency and throughput constraints.
• Score: 20.592217626952507
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: CRYSTAL-Kyber (Kyber) is one of the post-quantum cryptography (PQC) key-encapsulation mechanism (KEM) schemes selected during the standardization process. This paper addresses optimization for Kyber architecture with respect to latency and throughput constraints. Specifically, matrix-vector multiplication and number theoretic transform (NTT)-based polynomial multiplication are critical operations and bottlenecks that require optimization. To address this challenge, we propose an algorithm and hardware co-design approach to systematically optimize matrix-vector multiplication and NTT-based polynomial multiplication by employing a novel sub-structure sharing technique in order to reduce computational complexity, i.e., the number of modular multiplications and modular additions/subtractions consumed. The sub-structure sharing approach is inspired by prior fast parallel approaches based on polyphase decomposition. The proposed efficient feed-forward architecture achieves high speed, low latency, and full utilization of all hardware components, which can significantly enhance the overall efficiency of the Kyber scheme. The FPGA implementation results show that our proposed design, using the fast two-parallel structure, leads to an approximate reduction of 90% in execution time, along with a 66 times improvement in throughput performance.

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