CoDeCoVe: A Novel System Level Co-Design & Co-Verification Framework
Bibliographic record
Abstract
The challenge of designing a complex System-on-Chip (SoC) lies in the efficient division of the computation blocks between hardware and software implementation depending on design factors such as computational requirements for multiple complex algorithms, communication between the various sub-modules and frequency of memory access. This paper presents a novel hardware-software co-design and co-verification framework which allows the SoC computation modules to be designed and verified at five abstraction levels. Using High-Level Synthesis (HLS) design methodology, a high level system design is created based on the system specification and requirements. The design model is refined repeatedly from the higher to lower abstraction levels. At each design level, the correctness of the refinement process and the resulting models are verified thoroughly by creating testbenches at the top design level and refining them on each level by using the CoDeCoVe framework. The Fuzzy Vault-based Biometric Encryption System is used as a case study to evaluate the performance of the CoDeCoVe framework. The theoretical analysis and experimental results prove the correctness of the proposed framework.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".