ICAT: Engine to Perform Range Analysis and Allocate Bit-Widths for Arithmetic Datapaths
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Bibliographic record
Abstract
Range analysis determines allocation of fixed-point integer bit-widths, which is critical for arithmetic on fixed-point representations. The traditional methods, either simulation-based or static, can be time-consuming and produce coarse bounds, potentially leading to large error bounds and unnecessary bits. In this paper, we propose a new static method to perform fixed-point range analysis towards obtaining the tighter ranges efficiently. The hybrid method, ICAT, combines four techniques, including Interval arithmetic, consistency checking, affine arithmetic and arithmetic transform and is the only method that is aware how far it is from the exact solution. For the benchmarks available with comparable methods, we show that the bit-width allocation can be obtained with better results, and in shorter execution time.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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