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Accelerating NTT with RISC-V Vector Extension for Fully Homomorphic Encryption

by Alexandre Rodrigues - 2025.11.06

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Meetup 080

Abstract

We target the optimization of FHE by leveraging vectorized implementations in RISC-V processors, using the RISC-V Vector (RVV) Extension. In particular, we accelerate the Number Theoretic Transform (NTT) and the Inverse-NTT (INTT), integrating our implementations in OpenFHE.

The specific contributions of our paper are:

1) A systematic analysis of the performance and memory access patterns of four widely used NTT/INTT algorithms, namely Cooley-Tukey/Gentelman-Sande and Korn-Lambiotte/Pease;

2) RVV-optimized implementations of these algorithms, including modular arithmetic routines and tailored butterfly structures and root-of-unity accesses;

3) The integration of our vectorized implementations into the OpenFHE library;

4) A comprehensive performance evaluation using two distinct computational platforms - a Vector Processing Unit (VPU) running on an FPGA and an out-of-order core simulated in gem5.

Our results show that the Korn-Lambiotte/Pease implementations outperform Cooley-Tukey/Gentelman-Sande, which are commonly used by state-of-the-art libraries (e.g. Intel HEXL). Our approach achieves significant speedups in both NTT benchmarks and complete FHE applications.

About the speaker

Alexandre Rodrigues is currently pursuing a PhD in Computing from Instituto Superior Técnico’s doctoral programme in Electrical and Computer Engineering.

His research interests include performance modelling, microarchitectural design and RISC-V.

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