Design of a novel energy efficient topology for maximum magnitude generator
Bibliographic record
Abstract
A novel combinational digital device for finding maximum magnitude among the ‘ n ’ input numbers is proposed. This maximum magnitude generator (MaxMG) generates maximum magnitude as an output by utilising the bit by bit approach from multiple input (multi‐bit) values simultaneously. MaxMG generates output from most significant bit (MSB) to least significant bit (LSB) in parallel, which utilises a minimum number of gate counts among the multi‐bit of multiple input values. The minimum magnitude generator is also derived by applying the dual function to the MaxMG. The proposed design is implemented using Synopsys 90 nm generic library and RTL is written using Verilog HDL. The performance of the proposed design is compared with a rank based K th max selection algorithm, a parallel tree based maximum generator utilised comparator‐multiplexer combination, an array‐based maximum finder (AB) and improved quad tree (IQT). The bit by bit parallel processing at the inputs – from MSB to LSB, and the simple architecture utilising a minimum number of gates, makes the proposed design more energy efficient when compared with the K th max algorithm, the tree based maximum finder, the AB based maximum finder, and the IQT architecture.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".