International Association
for Cryptologic Research

Handwritten assembly is a widely used tool in the development of highperformance cryptography: By providing full control over instruction selection, instruction scheduling, and register allocation, highest performance can be unlocked. On the flip side, developing handwritten assembly is not only time-consuming, but the artifacts produced also tend to be difficult to review and maintain – threatening their suitability for use in practice.In this work, we present SLOTHY (Super (Lazy) Optimization of Tricky Handwritten assemblY), a framework for the automated superoptimization of assembly with respect to instruction scheduling, register allocation, and loop optimization (software pipelining): With SLOTHY, the developer controls and focuses on algorithm and instruction selection, providing a readable “base” implementation in assembly, while SLOTHY automatically finds optimal and traceable instruction scheduling and register allocation strategies with respect to a model of the target (micro)architecture.We demonstrate the flexibility of SLOTHY by instantiating it with models of the Cortex-M55, Cortex-M85, Cortex-A55 and Cortex-A72microarchitectures, implementing the Armv8.1-M+Helium and AArch64+Neon architectures. We use the resulting tools to optimize three workloads: First, for Cortex-M55 and Cortex-M85, a radix-4 complex Fast Fourier Transform (FFT) in fixed-point and floating-point arithmetic, fundamental in Digital Signal Processing. Second, on Cortex-M55, Cortex-M85, Cortex-A55 and Cortex-A72, the instances of the Number Theoretic Transform (NTT) underlying CRYSTALS-Kyber and CRYSTALS-Dilithium, two recently announced winners of the NIST Post-Quantum Cryptography standardization project. Third, for Cortex-A55, the scalar multiplication for the elliptic curve key exchange X25519. The SLOTHY-optimized code matches or beats the performance of prior art in all cases, while maintaining compactness and readability.

The U.S. National Institute of Standards and Technology (NIST) has designated ARM microcontrollers as an important benchmarking platform for its Post-Quantum Cryptography standardization process (NISTPQC). In view of this, we explore the design space of the NISTPQC finalist Saber on the Cortex-M4 and its close relation, the Cortex-M3. In the process, we investigate various optimization strategies and memory-time tradeoffs for number-theoretic transforms (NTTs).Recent work by [Chung et al., TCHES 2021 (2)] has shown that NTT multiplication is superior compared to Toom–Cook multiplication for unprotected Saber implementations on the Cortex-M4 in terms of speed. However, it remains unclear if NTT multiplication can outperform Toom–Cook in masked implementations of Saber. Additionally, it is an open question if Saber with NTTs can outperform Toom–Cook in terms of stack usage. We answer both questions in the affirmative. Additionally, we present a Cortex-M3 implementation of Saber using NTTs outperforming an existing Toom–Cook implementation. Our stack-optimized unprotected M4 implementation uses around the same amount of stack as the most stack-optimized Toom–Cook implementation while being 33%-41% faster. Our speed-optimized masked M4 implementation is 16% faster than the fastest masked implementation using Toom–Cook. For the Cortex-M3, we outperform existing implementations by 29%-35% in speed. We conclude that for both stack- and speed-optimization purposes, one should base polynomial multiplications in Saber on the NTT rather than Toom–Cook for the Cortex-M4 and Cortex-M3. In particular, in many cases, multi-moduli NTTs perform best.