Research on the Principle and Architecture of Icarus Verilog System
Bibliographic record
Abstract
As global technological competition intensifies, the challenges in chip design are becoming increasingly complex, highlighting the urgent need for open-source EDA (Electronic Design Automation) platforms. The introduction of open-source EDA tools can lower the barriers to chip design, foster scientific research, and promote talent development. However, issues such as a limited user base and insufficient contributions need to be addressed. This study investigates the 2022 version of Icarus Verilog, providing a detailed introduction to its system principles and analyzing its internal architecture and module composition. Additionally, we validate its preprocessing, compilation, and simulation functionalities by testing the ZUC-128 cryptographic algorithm on the LicheePi 4A, a high-performance RISC-V Linux development board based on the Lichee Module 4A and powered by the TH1520 core. Experimental results indicate that Icarus Verilog offers flexible open-source characteristics and a wide range of applications, reducing R&D costs and providing high utility. This research fills a gap in the domestic study of Icarus Verilog and offers valuable insights for the future development and optimization of open-source EDA tools.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".