Hog (HDL on git): a collaborative management tool to handle git-based HDL repository
Bibliographic record
Abstract
Abstract In this paper, we present Hog (HDL on git), a set of Tcl scripts and a suitable methodology to allow a fruitful use of git as a HDL repository and guarantee synthesis and placing reproducibility and binary file traceability. Tcl scripts, able to recreate the HDL projects are committed to the repository. This ensures that all the modifications done to the project are correctly propagated, allowing reproducibility. To make the system more user friendly, all the source files used in each project are listed in dedicated text files that are read out by the project Tcl file and imported into the project. Hog supports Xilinx Vivado, ISE (PlanAhead) and Intel Quartus. To guarantee binary file traceability, Hog links it permanently to a specific git commit by embedding the git-commit hash (SHA) into the binary file via HDL generics stored into firmware registers. This is done by means of a pre-synthesis script, which interacts with the git repository. The project creation and the pre/post synthesis Tcl scripts make use of the Hog utility library, that includes functions to handle git, parse tags, read list files, etc. Gitlab Continuous Integration (CI) is automatically configured by Hog to simulate, synthesise, and build the design. Hog-CI generates binary files and checks for timing violations. This permits validating new modifications before accepting them, by exploiting the Gitlab Merge Request (MR) system. This is meant to avoid the pollution of the official branch, undermining the starting point for other developers. Hog-CI runs on shared and private (where the needed IDE must be installed) Gitlab runners. It can parse MR parameters, allowing the specification of directives through special keywords in the MR title/description on Gitlab website.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.034 | 0.016 |
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".