Rapid RTL-based signal ranking for FPGA prototyping
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
As the capacity of integrated circuits increases, it is becoming increasingly difficult to ensure that a chip is free of design errors. Designers are increasingly turning to FPGA prototyping platforms to validate their designs much more extensively than is possible using simulation. A key challenge is one of visibility; signals can only be observed if they can be driven to pins of a chip. To enhance visibility during debug, designers regularly instrument their design with on-chip circuitry to record a small subset of signals at-speed for later off-chip analysis. The selection of which signals should be recorded critically affects the effectiveness of this approach. In this paper, we present an algorithm that ranks all signals in a design based on their predicted importance during validation. Compared to previous techniques, which analyze the circuit at the gate level, our algorithm works directly on the parse-tree representation of the circuit, and hence is orders of magnitude faster than these previous techniques. Our algorithm has been implemented as an integral part of Tektronix Certus, a commercial validation suite.
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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 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