ARIANNA: An Automatic Design Flow for Fabric Customization and eFPGA Redaction
Bibliographic record
Abstract
In the modern global Integrated Circuit (IC) supply chain, protecting intellectual property (IP) is a complex challenge, and balancing IP loss risk and added cost for theft countermeasures is hard to achieve. Using embedded configurable logic allows designers to completely hide the functionality of selected design portions from parties that do not have access to the configuration string (bitstream). However, the design space of redacted solutions is huge, with tradeoffs between the portions selected for redaction and the configuration of the configurable embedded logic. We propose ARIANNA, a complete flow that aids the designer in all the stages, from selecting the logic to be hidden to tailoring the bespoke fabrics for the configurable logic used to hide it. We present a security evaluation of the considered fabrics and introduce two heuristics for the novel bespoke fabric flow. We evaluate the heuristics against an exhaustive approach. We also evaluate the complete flow using a selection of benchmarks. Results show that using ARIANNA to customize the redaction fabrics yields up to 3.3× lower overheads and 4× higher eFPGA fabric utilization than a one-fits-all fabric as proposed in prior works.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".