Design of an energy efficient approximate BinDCT module in quantum cellular automata
Bibliographic record
Abstract
The quantum cellular automata (QCA) paradigm offers an ultra-low-power approach for realizing nanocomputing circuits at the molecular level, offering high parallelism capabilities. This study introduces a coplanar and energy-efficient implementation of the approximate binary discrete cosine transform (BinDCT) module using QCA technology. The proposed BinDCT module integrates various sequential and combinatorial submodules, including multiplexers (MUXs), demultiplexers (DeMUXs), parallel-in-parallel-out right-shift registers (PIPO-RSRs), ripple carry adders (RCAs), and ripple borrow subtractors (RBSs). Each submodule is systematically designed following the standard single-layer design principles, which are crucial for maximizing circuit performance, enhancing reliability, and minimizing power dissipation. Extensive simulations were conducted to validate the logic operation and energy dissipation of each submodule. The simulation results demonstrate a significant reduction in power dissipation- up to [Formula: see text] and an improvement in circuit area efficiency by [Formula: see text] compared to previous QCA implementations.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".