Chi-Ming Marvin Chung
NTT Multiplication for NTT-unfriendly Rings: New Speed Records for Saber and NTRU on Cortex-M4 and AVX2 📺
In this paper, we show how multiplication for polynomial rings used in the NIST PQC finalists Saber and NTRU can be efficiently implemented using the Number-theoretic transform (NTT). We obtain superior performance compared to the previous state of the art implementations using Toom–Cook multiplication on both NIST’s primary software optimization targets AVX2 and Cortex-M4. Interestingly, these two platforms require different approaches: On the Cortex-M4, we use 32-bit NTT-based polynomial multiplication, while on Intel we use two 16-bit NTT-based polynomial multiplications and combine the products using the Chinese Remainder Theorem (CRT).For Saber, the performance gain is particularly pronounced. On Cortex-M4, the Saber NTT-based matrix-vector multiplication is 61% faster than the Toom–Cook multiplication resulting in 22% fewer cycles for Saber encapsulation. For NTRU, the speed-up is less impressive, but still NTT-based multiplication performs better than Toom–Cook for all parameter sets on Cortex-M4. The NTT-based polynomial multiplication for NTRU-HRSS is 10% faster than Toom–Cook which results in a 6% cost reduction for encapsulation. On AVX2, we obtain speed-ups for three out of four NTRU parameter sets.As a further illustration, we also include code for AVX2 and Cortex-M4 for the Chinese Association for Cryptologic Research competition award winner LAC (also a NIST round 2 candidate) which outperforms existing code.
Polynomial Multiplication in NTRU Prime: Comparison of Optimization Strategies on Cortex-M4 📺
This paper proposes two different methods to perform NTT-based polynomial multiplication in polynomial rings that do not naturally support such a multiplication. We demonstrate these methods on the NTRU Prime key-encapsulation mechanism (KEM) proposed by Bernstein, Chuengsatiansup, Lange, and Vredendaal, which uses a polynomial ring that is, by design, not amenable to use with NTT. One of our approaches is using Good’s trick and focuses on speed and supporting more than one parameter set with a single implementation. The other approach is using a mixed radix NTT and focuses on the use of smaller multipliers and less memory. On a ARM Cortex-M4 microcontroller, we show that our three NTT-based implementations, one based on Good’s trick and two mixed radix NTTs, provide between 32% and 17% faster polynomial multiplication. For the parameter-set ntrulpr761, this results in between 16% and 9% faster total operations (sum of key generation, encapsulation, and decapsulation) and requires between 15% and 39% less memory than the current state-of-the-art NTRU Prime implementation on this platform, which is using Toom-Cook-based polynomial multiplication.