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Yet another Improvement of Plantard Arithmetic for Faster Kyber on Low-end 32-bit IoT Devices | | Junhao Huang
; Haosong Zhao
; Jipeng Zhang
; Wangchen Dai
; Lu Zhou
; Ray C.C. Cheung
; Cetin Kaya Koc
; Donglong Chen
; | | Date: |
1 Sep 2023 | | Abstract: | This paper presents another improved version of Plantard arithmetic that
could speed up Kyber implementations on two low-end 32-bit IoT platforms (ARM
Cortex-M3 and RISC-V) without SIMD extensions. Specifically, we further enlarge
the input range of the Plantard arithmetic without modifying its computation
steps. After tailoring the Plantard arithmetic for Kyber’s modulus, we show
that the input range of the Plantard multiplication by a constant is at least
2.45 times larger than the original design in TCHES2022. Then, two optimization
techniques for efficient Plantard arithmetic on Cortex-M3 and RISC-V are
presented. We show that the Plantard arithmetic supersedes both Montgomery and
Barrett arithmetic on low-end 32-bit platforms. With the enlarged input range
and the efficient implementation of the Plantard arithmetic on these platforms,
we propose various optimization strategies for NTT/INTT. We minimize or
entirely eliminate the modular reduction of coefficients in NTT/INTT by taking
advantage of the larger input range of the proposed Plantard arithmetic on
low-end 32-bit platforms. Furthermore, we propose two memory optimization
strategies that reduce 23.50% to 28.31% stack usage for the speed-version Kyber
implementation when compared to its counterpart on Cortex-M4. The proposed
optimizations make the speed-version implementation more feasible on low-end
IoT devices. Thanks to the aforementioned optimizations, our NTT/INTT
implementation shows considerable speedups compared to the state-of-the-art
work. Overall, we demonstrate the applicability of the speed-version Kyber
implementation on memory-constrained IoT platforms and set new speed records
for Kyber on these platforms. | | Source: | arXiv, 2309.00440 | | Services: | Forum | Review | PDF | Favorites | |
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