AutoNTT: Automatic Architecture Design and Exploration for Number Theoretic Transform Acceleration on FPGAs
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".