4 - Conception optimisée d'architectures en précision finie pour les applications de traitement du signal
Bibliographic record
Abstract
The new submicronic technologies offer real capacities in terms of integration of signal processing dedicated systems, images and digital communications. To control these new technologies, new design methods and new computer-aided design tools have appeared : the system design and the behavioral design. These methods offer an effective link between algorithm designers and circuit designers. But it creates also new methodological problems for design automation. Our study is in keeping with this process and is more particularly focused on transformation under constraints, from the abstract types (used in the declaration of variables for the behavioral specification) to the vector of bit types (used in the logical design). We illustrate our methodology by the use of the behavioral synthesis tool Gaut, developed in the Lester laboratory. We present the different models, analysis and methods used in a way to control computing noises in finite precision and real time architectures. Implementation of signal processing and image applications gives the efficiency and the importance of this approach in terms of architecture optimization.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".