Energy-Efficient Approximate Prewitt Filter Design
Bibliographic record
Abstract
Approximate computing (AC) is widely adopted as a new and emerging design paradigm for error-resilient applications, e.g., image processing, where sacrificed accuracy is utilized for design efficiency. The approximation is introduced within applications that include huge sensory-generated data or when the results of the application are visually consumed. Prewitt Filter is an efficient edge detector whose hardware implementation is based mainly on full adders (FAs), squaring units, and square root units. In this paper, we explore various approximate designs of the Prewitt Filter and evaluate different target area reductions, ranging from$5 \%$to$\text{5 5 \%}$. We define the different types of utilized FAs. For that, we utilize a set of five energy-efficient approximate FAs. The generated approximate Prewitt filters are suitable for image processing applications, where experiments have shown that the obtained designs achieve 5 % to 55 % reductions in area and power.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".