Efficient Address-Embedded Time-Domain Implementation of Minima Finders for Soft Error-Correction Decoders
Bibliographic record
Abstract
This brief presents a novel algorithm that identifies the first six smallest values and their positions in a vector of 256 quantized positive real numbers. The proposed technique is designed for efficient time domain implementation. It operates with signals that are specially structured to contain a pulse width representation of a real number and a pulse-code of its address. The signals are passed through a tree structure where the number of stages and the comparison blocks are devised to yield the best overall performance. To demonstrate the hardware efficiency of this technique, it was successfully implemented using TSMC 65-nm CMOS technology. Experimental results unequivocally showcase that the proposed “minima finder” (MF) algorithm outperforms state-of-the-art solutions in terms of latency and complexity. This makes the proposed architecture a compelling candidate for the hardware implementation of the next-generation high-throughput forward error-correction (FEC) decoders in the realm of fiber-optical communications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".