A Timing-Aware Configurable Adder Based on Timing Detection for Low-Voltage Computing
Bibliographic record
Abstract
Low-voltage computing effectively saves energy in circuit operations, but it suffers from an increasing propagation delay. Approximate computing can significantly reduce the propagation delay by using a simplified or improved circuit, albeit with an inevitable accuracy loss. To address these challenges, a timing-aware configurable adder (TACA) is proposed to achieve a good trade-off between energy efficiency and accuracy at low operating voltages. This design relies on the functions of timing-error detection and correction (TEDC) for the newly-proposed accuracy-configurable full adders (ACFAs). The ACFA operates in an exact mode and two approximate modes by using four transistors as power gating. The TEDC generates timing-error signals when the delay violates the timing constraint due to voltage overscaling. Then, an improved configuration scheme is developed to enable the ACFA to work in an approximate mode by allowing for error signals at runtime. This approximation shortens the carry propagation chain. Thus, the TACA is adapted to timing conditions at different supply voltages by reducing the propagation delay rather than the operation frequency.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".