A step towards intelligent translation from high-level design to RTL
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
Many researches have progressed to elaborate high level languages for system design. Nevertheless automatic refinement from high level to RTL can still not be automated and if designers can now specify their system at a high level, they are still forced to manually implement its RTL representation or use IP. We have developed an intermediate level language based on the representation of ASM charts with extensions such as user defined operators, communication channels, generic calls and recursivity but near the RTL level. This paper describes our compiler and presents our latest compilation results: the recursive Towers of Hanoi algorithm, various sort algorithms (included quick sort) and a mix of heap and merge sorts to implement fast parallel sort. These algorithms have been automatically synthesized in a FPGA and offer one to three orders of magnitude improvement compared to a pure software implementation for NoC. The tool is easily accessible to software or hardware designers and people from both communities will appreciate its high-level and cycle accurate approach.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it