Low power Wallace multiplier design based on wide counters
Bibliographic record
Abstract
SUMMARY Multiplication is one of the most basic arithmetic operations. It is used in digital applications, central processing units, and digital signal processors. In most systems, the multiplier lies within the critical path and hence, due to probability and reliability issues, the power consumption of the multiplier has become very important. Moreover, as chips shrink and their power densities increase, power is becoming a major concern for chip designers. The ever increasing demand for portable applications with their limited battery lifetime indicates that power considerations should be a center stone in today's designs and the future's designs. Thus, all this has motivated us to provide a novel circuit design technique for a low power multiplier without compromising the multiplier's speed. This paper presents a new power aware multiplier design based on Wallace tree structure. A new algorithm is proposed using high‐order counters to meet the power constraints imposed by mobility and shrinking technology. Commonly used multipliers of widths 8, 16, and 32 bits are designed based on the proposed algorithm. The new approach has succeeded in reducing the total number of gates used in the multiplier tree. Simulations on Altera's Quartus‐II FPGA simulator showed that the design achieves an average of 18.6% power reduction compared to the original Wallace tree. The design performs even better as the multiplier's size increases, achieving a 5% gate count reduction, a 26.5% power reduction, and a 23.9% better power‐delay product in 32‐bit multipliers. Copyright © 2011 John Wiley & Sons, Ltd.
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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) | 0.000 | 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 | 0.000 |
| 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 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".