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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".