On the Fixed-Point Accuracy Analysis and Optimization of Polynomial Specifications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Fixed-point accuracy analysis and optimization of polynomial data-flow graphs with respect to a reference model is a challenging task in many digital signal processing applications. Range and precision analysis are two important steps of this process to assign suitable integer and fractional bit-widths to the fixed-point variables and constant coefficients in a design such that no overflow occurs and a given error bound on maximum mismatch (MM) or mean-square-error (MSE) and signal-to-quantization-noise ratio (SQNR) is satisfied. This paper explores efficient optimization algorithms based on robust analyses of MM and MSE/SQNR for fixed-point polynomial data-flow graphs. Experimental results illustrate the robustness of our analyses and the efficiency of the optimization algorithms compared to previous work.
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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 | 0.001 | 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