23.7 A 16Gb/s 1 IIR + 1 DT DFE compensating 28dB loss with edge-based adaptation converging in 5µs
Bibliographic record
Abstract
I/O receivers routinely equalize ISI over 10 or more post-cursor UI. IIR DFEs are a low-power technique for canceling long post-cursor ISI tails, and have been demonstrated compensating over 20dB loss at fbit/2 up to 10Gb/s [1-5]. Equalizer adaptation is required to maintain signal integrity in time-varying channel and circuit conditions. Robust adaptation algorithms suitable for discrete-time (DT) DFEs are well-established, but there are few examples of adaptive algorithms for IIR DFEs [2,4], each exhibiting relatively slow convergence, additional high-bandwidth hardware and/or requiring the input data statistics to meet specific criteria. In this work, a 16Gb/s IIR DFE is integrated into a CDR, and the adaptation algorithm makes use of signals available in a regular binary phase detector (PD) to simultaneously adapt the IIR and DT taps. The novel algorithm provides faster and more robust convergence than has been previously demonstrated for IIR DFEs.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".