HPKA: A High-Performance CRYSTALS-Kyber Accelerator Exploring Efficient Pipelining
Bibliographic record
Abstract
CRYSTALS-Kyber (Kyber) was recently chosen as the first quantum resistant Key Encapsulation Mechanism (KEM) scheme for standardisation, after three rounds of the National Institute of Standards and Technology (NIST) initiated PQC competition which begin in 2016 and search of the best quantum resistant KEMs and digital signatures. Kyber is based on the Module-Learning with Errors (M-LWE) class of Lattice-based Cryptography, that is known to manifest efficiently on FPGAs. This work explores several architectural optimizations and proposes a high-performance and area-time (AT) product efficient hardware accelerator for Kyber. The proposed architectural optimizations include inter-module and intra-module pipelining, that are designed and balanced via FIFO based buffering to ensure maximum parallelisation. The implementation results show that compared to state-of-the-art designs, the proposed architecture delivers 25–51% speedups for Kyber's three different security levels on Artix-7 and Zynq UltraScale+ devices, and a 50–75% reduction in DSPs at comparable security level. Consequently, the proposed design achieve higher AT product efficiencies of 19–33%.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.007 | 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".