Hog (HDL on git): a collaborative management tool to handle git-based HDL repository
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
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.
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 | 0.000 | 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 | 0.000 | 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