Bibliographic record
Abstract
Design verification is an important though time-consumingaspect of hardware design. A good testbench should supportperforming functional coverage of a design by making it easy to implement tests and determine which tests are being performed. However, for complex designs, creating and main-taining effective testbenches can take increasing amounts of time away from actual design. A further complication is there may be two development flows: conventional hardware written in a hardware description language (HDL) such as Verilog orVHDL and high-level synthesis (HLS). In the HLS approach, the hardware is specified in a higher-level language (HLL) and then converted to an HDL through HLS tools. In this flow, testbenches for the design are written in the same HLLand cosimulation is used to verify the generated HDL. Due totool restrictions, cosimulation may not always work. In VivadoHLS [1] for example, the design must contain control signals to define when to start and stop the module or the initiation interval for new data must be one cycle. Without cosimulation, the user must write an HDL testbench manually in addition to a testbench in the HLL for preliminary verification. To simplify writing testbenches, we present Sonar: an open-source Python library to write cross-language testbenches. From a common source script, Sonar can generate testbenches written in SystemVerilog (SV) and C++. These files can then be imported into standard simulation tools such as ModelSim[2] or Vivado HLS and run. The use of Python makes it easy to extend Sonar with higher layers of abstraction for testbenches and integrate it with other software platforms.Sonar is available at https://github.com/UofT-HPRC/sonar.
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.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.071 | 0.035 |
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".