Bibliographic record
Abstract
This paper presents a versatile bit-error-rate (BER) testing scheme to characterize the quality of communication interfaces. Traditionally, the presilicon BER is evaluated using time-consuming software simulations. The stand-alone BER test products for postsilicon evaluation are expensive and do not include channel emulators, which are essential to testing the BER under the presence of noise. For both the design and evaluation phases, we present a scheme for BER testing in field-programmable gate arrays (FPGAs) that consists of a BER tester (BERT) core and a novel additive white Gaussian noise (AWGN) generator core. The maximum output value of our AWGN generator is 53, whereas that of the existing solutions is less than 7. Therefore, our generator can better emulate the tail of a Gaussian distribution, which is suitable for exploring applications at very low BERs. We also present a pipelined structure that exploits the central limit theorem for speedups of four or more. Combining a BERT and an AWGN in FPGAs is orders of magnitude more efficient in cost, volume, and energy over the existing similar-speed stand-alone solutions and has a huge speed advantage over software simulations. We demonstrate the applications of our solution through two case studies.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".