A Hardware Accelerator for Fully Homomorphic Encryption based Machine Learning Applications
Bibliographic record
Abstract
Fully homomorphic encryption (FHE) is a lattice-based cryptography scheme that enables mathematical operations to be performed on encrypted data without first having to decrypt the data, allowing for the implementation of secure machine learning (ML). As performing ML applications with FHE-encrypted data using current processors can take significant computation time, DARPA has created the DPRIVE challenge for the design of an FHE hardware accelerator. In this thesis, we design and verify through a cycle-accurate Verilog model both residue number system (RNS) and large arithmetic word size (LAWS) implementations of a hardware accelerator capable of realizing the DPRIVE performance specifications for 1024-point logistic regression using the Cheon-Kim-Kim-Song (CKKS) mathematical foundation. We show that the two implementations achieve comparable execution time performance, and that the estimated CMOS ASIC implementation area of the LAWS design requires only 68% of the core area of the equivalent RNS design in an equivalent technology node.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".