Hardware Acceleration for High-Volume Operations of CRYSTALS-Kyber and CRYSTALS-Dilithium

Xavier Carril, Charalampos Kardaris, Jordi Ribes-González, O. Farràs, Carles Hernández, Vatistas Kostalabros, Joel Ulises González-Jiménez, Miquel Moretó
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Abstract

Many high-demand digital services need to perform several cryptographic operations, such as key exchange or security credentialing, in a concise amount of time. In turn, the security of some of these cryptographic schemes is threatened by advances in quantum computing, as quantum computer could break their security in the near future. Post-Quantum Cryptography (PQC) is an emerging field that studies cryptographic algorithms that resist such attacks. The National Institute of Standards and Technology (NIST) has selected the CRYSTALS-Kyber Key Encapsulation Mechanism and the CRYSTALSDilithium Digital Signature algorithm as primary PQC standards. In this paper, we present FPGA-based hardware accelerators for high-volume operations of both schemes. We apply High-Level Synthesis (HLS) for hardware optimization, leveraging a batch processing approach to maximize the memory throughput, and applying custom HLS logic to specific algorithmic components. Using reconfigurable field-programmable gate arrays (FPGAs), we show that our hardware accelerators achieve speedups between 3x and 9x over software baseline implementations, even over ones leveraging CPU vector architectures. Furthermore, the methods used in this study can also be extended to the new CRYSTALS-based NIST FIPS drafts, ML-KEM and ML-DSA, with similar acceleration results.
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为 CRYSTALS-Kyber 和 CRYSTALS-Dilithium 的大容量运行提供硬件加速
许多高需求的数字服务需要在短时间内执行若干加密操作,如密钥交换或安全认证。反过来,量子计算的进步也威胁到了其中一些加密方案的安全性,因为量子计算机可能在不久的将来破坏它们的安全性。后量子密码学(PQC)是一个新兴领域,研究可抵御此类攻击的密码算法。美国国家标准与技术研究院(NIST)已选定 CRYSTALS-Kyber 密钥封装机制和 CRYSTALSDilithium 数字签名算法作为 PQC 的主要标准。在本文中,我们介绍了基于 FPGA 的硬件加速器,用于这两种方案的大批量操作。我们将高级合成(HLS)应用于硬件优化,利用批处理方法最大限度地提高内存吞吐量,并将定制 HLS 逻辑应用于特定算法组件。通过使用可重新配置的现场可编程门阵列(FPGA),我们展示了我们的硬件加速器比软件基线实现提高了 3 到 9 倍的速度,甚至超过了利用 CPU 向量架构的实现。此外,本研究中使用的方法还可以扩展到基于 CRYSTALS 的新 NIST FIPS 草案、ML-KEM 和 ML-DSA,并获得类似的加速结果。
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