Bibliographic record
Abstract
This thesis tackles the problem of data conversion in the Residue Number System (RNS). The RNS has been considered as an interesting theoretical topic for researchers in recent years. Its importance stems from the absence of carry propagation between its arithmetic units. This facilitates the realization of high-speed, low-power arithmetic. This advantage is of paramount importance in embedded processors, especially those found in portable devices, for which power consumption is the most critical aspect of the design. However, the overhead introduced by the data conversion circuits discourages the use of RNS at the applications. In this thesis, we aim at developing efficient schemes for the conversion from the conventional representation to the RNS representation and vice versa. The conventional representation can be in the form of an analog continuous-time signal or a digital signal represented in binary format. We present some of the currently available algorithms and schemes of conversion when the signal is in binary representation. As a contribution to this field of research, we propose three different schemes for direct conversion when interaction with the real analog world is required. We first develop two efficient schemes for direct analog-to-residue conversion. Another efficient scheme for direct residue-to-analog conversion is also proposed. The performance and the efficiency of theses converters are demonstrated and analyzed. The proposed schemes are aimed to encourage the utilization of RNS in various real-time and practical applications in the future.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".