Paper 2025/2310 Original: eprint.iacr.org/2025/2310 ↗

RACE: A Rapid ARM Cryptographic Engine for Code-Based Classic McEliece PQC Scheme

Abstract

With the rapid development of quantum computing, traditional public-key cryptosystems are increasingly vulnerable, making post-quantum cryptography (PQC) a critical area for securing future information systems. As a prominent code-based key encapsulation mechanism (KEM), Classic McEliece offers strong quantum security. However, its large public key size and complex decoding process introduce significant performance bottlenecks, hindering its practical deployment on mobile and edge devices. To address these challenges, we propose RACE (Rapid ARM Cryptographic Engine), a systematic acceleration framework tailored for ARMv8 architectures, designed to enhance the efficiency of Classic McEliece while preserving its constant-time security guarantees. In the key generation phase, we introduce an implicit data layout and in-place transformation strategy based on LUP decomposition, coupled with NEON SIMD vectorization. For the encapsulation phase, we optimize error vector generation with constant-time techniques and batch-loading strategies to reduce memory access redundancy during matrix-vector multiplications. In the decapsulation phase, we apply lane-level fusion and dual-lane butterfly fusion techniques, leveraging NEON instructions to parallelize field multiplication and fast fourier transform (FFT) butterfly operations. Experiments on three ARMv8 platforms (Kunpeng 920, Apple M1, Apple M2 Pro) demonstrate significant speedups of RACE compared to the official vec implementation. For the McEliece8192128 parameter set, key generation is accelerated by 1.33$\times$, 1.90$\times$, and 2.04$\times$, encapsulation by 1.33$\times$, 1.23$\times$, and 1.26$\times$, and decapsulation by 1.27$\times$, 1.68$\times$, and 1.66$\times$, respectively. RACE also outperforms open-source libraries, particularly in decapsulation, where speedups reach several hundred times. These results validate the practical applicability and deployment potential of RACE in mobile and edge computing environments.