Adaptable FWHW Formal Co-Verification of SoC RISC-V Components
Bibliographic record
Abstract
The increasing shift towards the RISC-V open-source instruction set architecture requires the development of new design techniques. In recent years, it has been demonstrated that RISC-V designs can be generated in a modular and scalable manner by utilizing metamodeling techniques. However, verifying these designs presents a significant challenge, because the verification must consider both Register Transfer Level (RTL) components and firmware components such as drivers. Furthermore, the interaction between firmware and hardware components is susceptible to various issues, including incorrect transaction sequences, synchronization problems, encoding mismatches, and reserved values. Traditionally, verifying the interaction between hardware and firmware requires simulation/emulation tools and verification engineers with expertise in both firmware and hardware. To overcome these challenges, this paper introduces an automated formal verification approach for FWHW Co-verification of peripherals such as timers and interrupt controllers, and their respective drivers in generated RISC-V designs. This verification process employs formal verification methods. This methodology enables the detection of bugs in both hardware and firmware because it consists of the verification of individual components that can be reused later in the integration process. By implementing this methodology in the early design stages, developers can identify and address potential issues more efficiently and avoid later corrections.
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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 | 0.002 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".