Arithmetic compaction circuits for mixed-signal systems testing
Bibliographic record
Abstract
Arithmetic error-control codes (ECCs) have been designed to protect the integrity of data being transmitted and/or processed. The implementation of an ECC involves constructing an appropriate encoding/decoding device. An important part of this device is a residue computing circuit (RCC). This circuit has also been used in mixed-signal systems testing and is referred to as a compaction circuit. As ECCs originated primarily to protect data transfers over binary channels, the design methodology for RCCs has been mostly oriented toward a binary case. A non-binary design technique has only been reported for a special type of compaction modulus. In this work, we consider a design technique for a multiple-bit arithmetic compaction circuit with an arbitrary compaction modulus. It is assumed that the codes being compacted are fuzzy, which distorts the result of compaction and increases the aliasing rate. Even though the fault free system's output code distortion is small, the compaction circuit may aggravate it beyond the acceptable levels making the method impractical. We design a low cost compactor that does not increase the code distortion. The circuit can be used for off-line and on-line mixed-signal systems testing, as well as fault-tolerant data processing and noise-tolerant data transmission.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".