AutoNTT: Automatic Architecture Design and Exploration for Number Theoretic Transform Acceleration on FPGAs

Fully Homomorphic Encryption (FHE), which enables homomorphic computing on encrypted data, has emerged as a promising privacy-aware computing method. However, FHE is orders-of-magnitude slower than the same computation on plain data, making it far from practical use. One of the major computation bottlenecks in FHE is the Number Theoretic Transform (NTT). While prior studies have accelerated NTT using specific architectures and FHE parameters, there still lacks a design automation tool to systematically design and explore various NTT architectures to support a diverse range of FHE parameters, such as various polynomial sizes, modulo sizes, and reduction methods. In this paper, we present AutoNTT, an open-source automatic architecture design and exploration tool to generate highly scalable NTT accelerators on FPGAs. Unlike prior studies, AutoNTT can automatically generate several optimized NTT acceleration architectures in HLS (i.e., iterative, dataflow, and hybrid architectures) with multiple common reduction methods, and support a large range of polynomial sizes (2¹⁰–2¹⁷) and modulo sizes ($log_{q}: 28-64$). In our auto-generated NTT architectures, we have applied many optimizations, such as polynomial and twiddle factor buffer reduction, and simplifying interconnections between different butterfly unit groups. Compared to prior studies, AutoNTT can generate NTT accelerators with 2.48× better latency and 3.61× better throughput on average, while maintaining a similar FPGA resource utilization. AutoNTT will be released soon at https://github.com/SFU-HiAccel/AutoNTT.