Design and Implementation of a Secure RISC-V Microprocessor
Bibliographic record
Abstract
Secret keys can be extracted from the power consumption or electromagnetic emanations of unprotected devices. Traditional countermeasures have a limited scope of protection and impose several restrictions on how sensitive data must be manipulated. We demonstrate a bit-serial RISC-V microprocessor implementation with no plain-text data. All values are protected using Boolean masking. Software can run with little to no countermeasures, reducing code size and performance overheads. Unlike previous literature, our methodology is fully automated and can be applied to designs of arbitrary size or complexity. We also provide details on other key components, such as clock randomizer, memory protection, and random number generator (RNG). The microprocessor was implemented in 65-nm CMOS technology. Its implementation was evaluated using NIST tests and side-channel attacks. Random numbers generated with our RNG pass on all NIST tests. The side-channel analysis on the baseline implementation extracted the advanced encryption system (AES) key using only 375 traces, while our secure microprocessor was able to withstand attacks using 20M traces.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".