FLASH: An Abstract Machine for Modelling Fully Homomorphic Encryption Accelerators
Bibliographic record
Abstract
Fully Homomorphic Encryption (FHE) enables computation directly on encrypted data, and multiple hardware accelerators have emerged. Unfortunately, each accelerator is evaluated using a rigid and proprietary simulator. This makes it challenging to compare accelerators or evaluate the performance impact of individual ideas. Perhaps more importantly, it has proven to be challenging to transfer ideas across accelerators. Each new idea in FHE typically requires changes across the stack, from the security protocol (e.g., KeySwitching), to the memory hierarchy (e.g., on-the-fly key reuse), and individual functional units (e.g., FFT-based polynomial multipliers). Currently, there is no tool for exploring FHE accelerators. We introduce FLASH, an extensible, modular, and opensource simulator for FHE accelerators. We pioneer a new methodology for constructing architecture simulators-compilerlike dialects (or intermediate representations). FLASH includes three distinct yet interoperable dialects for writing imperative code on encrypted types (Security), expressing spatial and temporal data movement of polynomials (PolyDataflow), and creating custom functional units (Modular arithmetic). A transpiler seamlessly ties these dialects, and rapidly high-level-synthesizes (HLS) them to accelerator-specific performance model. Users can implement algorithm-hardware co-design ideas, as transformations of kernels written in our dialects, and do not require time-consuming buggy simulator rewrites. We demonstrate the versatility of FLASH using multiple case studies: i) we conduct the first pubic head-to-head comparison of multiple FHE accelerators. ii) we backport ideas from multiple papers and quantify their impact (e.g., KeySwitching). We can construct an FHE accelerator model from scratch with $\simeq \mathbf{5 0 0}$ lines-of-code (vs. 3000+ when working with existing FHE library and simulator). iii) Finally, we show that we can implement new ideas with modular changes ($\lt50$ lines of code). The dialects helps us affect the changes in a targeted and concise manner.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".