DVS: an object-oriented framework for distributed Verilog simulation
Bibliographic record
Abstract
There is a wide spread usage of hardware design languages (HDL) to speed up the time to market for the design of modern digital systems. Verification engineers can simulate hardware in order to verify its performance and correctness with help of an HDL. However, simulation can't keep pace with the growth in size and complexity of circuits and has become a bottleneck of the design process. Distributed HDL simulation on a cluster of workstations has the potential to provide a cost effective solution to this problem. We describe the design and implementation of DVS, an object-oriented framework for distributed Verilog simulation. Verilog is an HDL which sees wide industrial use. DVS is an outgrowth of clustered time warp, originally developed for logic simulation. The design of the framework emphasizes simplicity and extensibility and aims to accommodate experiments involving partitioning and dynamic load balancing. Preliminary results obtained by simulating a 16 bit multiplier are presented.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".