Analysis of range and precision for fixed-point linear arithmetic circuits with feedbacks
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Bibliographic record
Abstract
Analysis of range and precision is always an important task for high level synthesis and verification. Although several researches have been dedicated to these two problems, in the case of linear fixed-point arithmetic circuits with feedbacks such as an Infinite Impulse Response (IIR) filter, conventional approaches are either constituting major overestimations or cannot handle arbitrary order feedback circuits. In this paper we focus on this problem and propose two efficient heuristics for range and precision analysis of such circuits, when the input and error bounds are given. The methods can be used for efficient integer and fractional bit-width allocation in the optimization flow. Moreover, for the purpose of module reusability and matching, verification algorithms have been proposed. Experimental results prove robust computations of range and precision.
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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 | 0.000 | 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 | 0.000 | 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 it