Matrix: Multi-Cipher Structures Dataflow for Parallel and Pipelined TFHE Accelerator
Bibliographic record
Abstract
Fully homomorphic encryption over torus (TFHE) enables the execution of arbitrary functions on encrypted data through programmable bootstrapping (PBS). However, performing all operations on ciphertext during PBS results in high computational and memory requirements, limiting the deployment of PBS in real-world scenarios. Previous TFHE accelerator designs have attempted to improve performance by employing specific dataflow and functional units, but these techniques may require large off-chip bandwidth or on-chip storage when scaling up computation capacity. Additionally, the design of specialized functional units may limit the utilization of computation units when facing dynamic secure parameter settings. To address these challenges and further improve PBS throughput in TFHE, we propose Matrix , an ASIC-based architecture that balances off-chip bandwidth and on-chip storage according to the execution flow of PBS. In Matrix , we utilize a unified special-prime-based processing element (PE) that achieves high utilization with minimal resource overhead. Furthermore, we propose a hybrid PBS dataflow that can efficiently reduce computation complexity and memory requirements. Compared to state-of-the-art TFHE accelerators, Matrix achieves 1.43 × -5.66 × throughput improvement for PBS. For ZAMA Deep-NN benchmark, we achieve 525.60× and 68.06× speedup compared to CPU and GPU, respectively. 1
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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 | 0.000 |
| 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) | 0.008 | 0.001 |
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".