Verilog Loop Unrolling, Module Generation, Part-Select and Arithmetic Right Shift Support in Odin II
Bibliographic record
Abstract
Verilog is a hardware description language (HDL) that supports the specification of hardware circuitry and control logic for production, simulation and testing. The subset of the specification used for production is called synthesizable. Verilog-to-Routing (VTR) is a Computer-Aided Design (CAD) flow. It transforms synthesizable Verilog into a placed and routed configuration for a Field Programmable Gate Array (FPGA) architecture specified in XML. The front end of the VTR CAD flow is Odin II. Odin II parses Verilog files and uses them to create a netlist consisting of inputs, outputs, nodes, and connections. Odin II is an open-source research project, and full Verilog language coverage is a work in progress. This work extends Odin II's Verilog support to files containing the arithmetic right shift operator (>>>) and both the + : and - : part-select operators. It also adds support for simple for loops, while loops and loop-based module generation. Dynamic looping constructs are not synthesizable, so all looping constructs are processed before the netlist is generated. This paper will present the missing language features that were implemented, the scope of their implementation, the architecture of the solution, testing and finally the efficiency of the contributions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".