Highly Accurate and Energy Efficient Binary-Stochastic Multipliers for Fault-Tolerant Applications
Bibliographic record
Abstract
Stochastic circuits use randomly distributed bitstreams to represent numbers, so leading to small areas and low power dissipation. However, it does not only result in a long latency and thus increases energy, but also reduces computing accuracy. In this brief, a design of parallel stochastic multipliers with high accuracy and low energy is proposed. To this end, an algorithm for finding optimal multiplicative bitstreams (OpMulbs) is developed for multipliers. Experimental results show that the proposed parallel multipliers using OpMulbs are the most accurate among currently available stochastic multipliers. They also require much less energy with a smaller latency, compared to the others. With a mean squared error of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4.02{\times } 10^{-6}$ </tex-math></inline-formula> , the proposed 8-bit multiplier shows a 42.18%, 48.15%, 20.35%, and 55.56% reduction in area, power, latency, and energy, respectively, compared to an 8-bit exact binary multiplier. The applications in multiply-accumulate units and image processing algorithms show that the proposed multipliers outperform the state-of-the-art stochastic designs in several considered criteria and binary designs in hardware cost.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".