Design and Analysis of A Majority Logic Based Imprecise 6–2 Compressor for Approximate Multipliers
Bibliographic record
Abstract
Approximate computing is an emerging paradigm for trading off computing accuracy to reduce energy consumption and design complexity in a variety of applications, for which exact computation is not a critical requirement. Different from conventional designs using AND-OR and XOR gates, the majority gate is widely used in many emerging nanotechnologies. An ultra-efficient 6–2 compressor is proposed in this paper. It is composed of two majority gates that lead to low energy consumption and high hardware efficiency. The proposed compressor is utilized in the approximate partial product reduction of a modified <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\boldsymbol{8\times 8}$</tex> Dadda multiplier with a truncated structure. Experimental results show that this multiplier realizes a significant reduction in hardware cost, especially in terms of power and area, on average by up to 40% and 31% respectively, compared to exact and state-of-the-art designs. The application of image multiplication is also presented to assess the practicability of the multiplier. The results show that the proposed multiplier results in images with higher quality in peak signal to noise ratio (PSNR) and mean structural similarity index metric (MSSIM) compared to other designs.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".