Bibliographic record
Abstract
Approximate computing (AC) is an emerging computing paradigm for energy efficiency. Typically, AC is implemented at the primary arithmetic level, e.g., addition, multiplication, and division, and its performance is evaluated by integration within an application. However, the achieved design efficiency may not be satisfactory. Therefore, for a specific approximate application, we need to study the most suitable settings of its basic approximate component. In this paper, we investigate several approximate designs of the Sobel filter, which is used for image edge detection. We consider different target designs, e.g., for 25% area reduction, we determine the various types of the used full adders and the number of components for each type. For an approximate Sobel filter with 15% to 55% area and power reduction compared to the exact design, we determine the settings for each target design. The obtained Sobel designs are evaluated for different benchmark images, i.e., Cameraman, Lena, and Bikesgray, and show a highly acceptable quality for edge detection. The average multiscale structural similarity (MSSSIM) index for all evaluated designs on the three benchmark images was 0.73.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".