Automation of Communication Refinement and Hardware Synthesis within a System-Level Design Methodology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Traditional register-transfer level design methodologies for systems-on-chip are failing to keep up with the growing complexity of embedded applications and architectures. A well-known solution is to raise the level of design abstraction by using system-level methodologies. The refinement from system-level specifications to concrete implementations is an essential step in a system-level design methodology. This article presents a novel methodology for the refinement from transaction-level communications to pin- and cycle-accurate protocols as well as the generation of synthesizable hardware from system-level specifications. Automatic communication refinement and hardware synthesis were successfully applied to a rover guiding system. Hand-coded and automatically generated register-transfer level modules of the rover are compared. Results show that a hardware/software implementation of the guiding system using generated register-transfer level modules has overheads of less than one percent in latency and hardware area when compared to an implementation using hand-coded modules.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it