Approximate XOR/XNOR-based adders for inexact computing
Bibliographic record
Abstract
Power dissipation has become a significant issue for integrated circuit design in nanometric CMOS technology. To reduce power consumption, approximate implementations of a circuit have been considered as a potential solution for applications in which strict exactness is not required. In inexact computing, power reduction is achieved through the relaxation of the often demanding requirement of accuracy. In this paper, new approximate adders are proposed for low-power imprecise applications. These adders are based on XOR/XNOR gates with multiplexers implemented by pass transistors. The proposed approximate XOR/XNOR-based adders (AXAs) are evaluated and compared with respect to energy consumption, delay, area and power delay product (PDP) with an accurate full adder. The metric of error distance is used to evaluate the reliability of the approximate designs. Simulation by Cadence's Spectre in TSMC 65nm process has shown that the proposed designs consume less power and have better performance (such as a lower propagation delay) compared to the accurate XOR/XNOR-based adder, while the error distance remains similar or better than other approximate adder designs.
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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 | 0.001 | 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".