Quasi-static energy recovery binary decision diagram logic (QSBDDL)
Bibliographic record
Abstract
Due to the dynamic nature of their operation, most adiabatic logic families feature high switching activity. Furthermore, long latency caused by gate level pipelining of adiabatic circuits is unacceptable in a number of DSP applications. In this thesis, a new adiabatic logic style, named Quasi-Static Energy Recovery Binary Decision Diagram Logic (QSBDDL), is proposed. It remedies both of the above mentioned problems by combining quasi-static operation with complex logic gates and partial energy recovery and can be used in the implementation of arithmetic units in low power DSP systems. To illustrate the design style, an 8 x 8 QSBDDL multiplier, featuring a novel partial product reduction architecture, was designed and implemented in a 0.18μm CMOS process. At a clock frequency of 100MHz, the implemented multiplier uses 50% and 20% less energy than equivalent multipliers implemented using conventional static CMOS circuits and quasi-static energy recovery logic (QSERL), respectively. Furthermore, the latency of the QSBDDL multiplier is reduced by a factor of 2.4 as compared to that of the QSERL multiplier.
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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 | 0.001 |
| 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".