HW/SW Formal Co-Verification of Rust-based Designs Using Hardware Abstraction Model
Bibliographic record
Abstract
In recent years, Rust has emerged as a powerful programming language, offering significant advantages over traditional design languages such as C and $\mathrm{C}++$. Rust’s features, including memory safety, concurrency without data races, and a strong type system, make it uniquely suited for developing reliable and efficient embedded systems. Despite these benefits, formal hardware-software co-verification methods have not kept pace with the advancements in Rust. Current co-verification approaches often struggle with complexity and insufficient integration between hardware and software components, leading to incomplete verification processes and potential undetected bugs. To address these challenges, we propose a novel approach by constructing a Rust-based hardware abstraction model that seamlessly integrates both hardware and software verification. This model leverages Rust’s inherent safety features to facilitate a more robust co-verification process. Additionally, we have developed a comprehensive hardware-software co-verification framework that can be deployed throughout the entire development life cycle, from initial design to final deployment. This framework ensures continuous and thorough verification, significantly reducing the likelihood of undetected bugs. We applied our proposed framework to several industrial and open-source designs, demonstrating its effectiveness in identifying multiple bugs in a significantly reduced time frame. The results highlight the efficiency and reliability of our Rust-based hardware-software co-verification framework, paving the way for more secure and robust system designs in the future.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".