A non-parametric approach for dynamic range estimation of nonlinear systems
Bibliographic record
Abstract
It has been widely recognized that the dynamic range information of an application can be exploited to reduce the datapath bitwidth of either processors or ASICs, and therefore the overall circuit area, delay, and power consumption. Recent advances in analytical dynamic range estimation methods indicate that by systematically decomposing the system inputs into orthonormal random variables using a mathematical procedure called polynomial chaos expansion (PCE), output statistics of interest can be obtained for both linear and nonlinear systems. Despite its power for capturing both spatial and temporal correlation, the application of this method has been limited only to near-Gaussian inputs. In this paper, we propose the first algorithm with the capacity of handling both near-Gaussian and non-Gaussian input signals. Our method is based on the use of independent component analysis (ICA). Our experiments show that the new algorithm can reduce the original relative errors of 2nd order moments from 25% - 65% to 1% - 2%.
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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 | 0.000 | 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 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".