Max-Pooling PD: A Machine Learning-Based Timing Recovery Method
Bibliographic record
Abstract
This paper presents a max-pooling phase detector (PD) designed to significantly reduce power consumption and area compared to conventional baud-rate detectors such as the Mueller-Mueller (MMPD) and Sign-Sign MMPD (SS-MMPD). The PD was originally developed using a convolutional neural network (CNN) model and trained within a machine-learning (ML) framework optimized for the provided training dataset. The design method was completed using a minimum mean square error (MMSE) approach to address discrepancies between training conditions and real-world operation, enabling traditional adaptation methods to enhance the performance and robustness of the PD. Synthesized in the GF 22-nm FD-SOI process, the max-pooling PD consumes 40% less power and occupies 60% less area than a standard SS-MMPD.
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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 | 0.000 | 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 | 0.000 | 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".