Highly accurate division and square root circuits by exploiting signal correlation in stochastic computing
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Stochastic computing (SC) is an approximate computing paradigm using probabilities and aims at realizing circuits with low hardware cost. Basic operations (such as addition) have been comprehensively studied, whereas there are few studies on nonlinear operations (such as division and square root) in SC. In this paper, a stochastic division circuit is proposed by using maximally correlated input bitstreams to eliminate the necessity for distinguishing the divisor and dividend. Additionally, four stochastic square root circuits are designed with improved accuracy by decreasing the correlation between intermediate bitstreams via inserting delay elements. Experimental results show that both the proposed division and square root circuits achieve lower mean squared errors (MSEs) while requiring nearly the same hardware resources, compared with the state‐of‐the‐art designs. This result shows the potential in exploiting signal correlation in SC circuit design for high accuracy.
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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 | 0.002 | 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) | 0.000 | 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 it