Leading one detectors and leading one position detectors - An evolutionary design methodology
Bibliographic record
Abstract
Design of leading-one detector (LOD) and leading-one position detector (LOPD) are important as they are used for the normalization process in floating-point multiplication, floating-point addition/subtraction and in logarithmic converters. In this paper, the authors propose various gate-level architectures for LOD and LOPD. The LOD and LOPD circuits are evolved using the evolutionary algorithm (EA) and using the evolved lower-order gate structures, various higher-order circuits are constructed. To obtain better results, the EA is modified and a novel shuffling operation is performed to prevent the algorithm from settling in the local minima. Then the constructed LOD and LOPD circuit is synthesized using Cadence® RTLCompiler® using TSMC 180nm library. The LOD and LOPD circuits can be implemented in an Application Specific Integrated circuit (ASIC) or in a Field Programmable Gate Array (FPGA), and hence it is independent of the technology library. Perhaps the evolution can also be made as an intrinsic process during the application run time and the evolved best gate structure can be chosen. We restrict this paper to the extrinsic evolution of LOD and LOPD gate level architectures.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".