A low-power, high-performance approximate multiplier with configurable partial error recovery
Bibliographic record
Abstract
Abstract—Approximate circuits have been considered for error-tolerant applications that can tolerate some loss of accuracy with improved performance and energy efficiency. Multipliers are key arithmetic circuits in many such applications such as digital signal processing (DSP). In this paper, a novel approximate multi-plier with a lower power consumption and a shorter critical path than traditional multipliers is proposed for high-performance DSP applications. This multiplier leverages a newly-designed approximate adder that limits its carry propagation to the nearest neighbors for fast partial product accumulation. Different levels of accuracy can be achieved through a configurable error recovery by using different numbers of most significant bits (MSBs) for error reduction. The approximate multiplier has a low mean error distance, i.e., most of the errors are not significant in magnitude. Compared to the Wallace multiplier, a 16-bit approximate multiplier implemented in a 28nm CMOS process shows a reduction in delay and power of 20 % and up to 69%, respectively. It is shown that by utilizing an appropriate error recovery, the proposed approximate multiplier achieves similar processing accuracy as traditional exact multipliers but with significant improvements in power and performance. I.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".