A 112-Gb/s PAM-4 Low-Power Nine-Tap Sliding-Block DFE in a 7-nm FinFET Wireline Receiver
Bibliographic record
Abstract
Practical realization of decision feedback equalizers (DFEs) has to date been limited to at most two taps in 100-Gb/s long-reach (LR) wireline applications due to significant power, area, and timing costs. This article presents a systolic many-tap low-complexity sliding-block decision feedback equalizer (SB-DFE) that overcomes the implementation challenges of conventional DFEs with no performance loss. A nine-tap configuration is demonstrated in a 112-Gb/s analog-to-digital converter (ADC)-digital signal processing (DSP) four-level pulse amplitude modulation (PAM-4) LR wireline receiver implemented in 7-nm FinFET. The architecture partitions the received signal into overlapping but computationally independent blocks thereby breaking the feedback loop of the DFE and allowing logic pipelining. Unlike existing feedback-breaking techniques, the computational overhead of the SB-DFE can be made arbitrarily small for any tap count—indeed, we show the practicality of SB-DFE implementations exceeding 30 taps. Optimized pipeline cuts are employed to minimize the latency through the SB-DFE while maintaining timing margin. The nine-tap SB-DFE is paired with a five-precursor tap feedforward equalizer (FFE) and compared to a two-tap-DFE 15-tap-FFE reference DSP implemented in the same receiver. A bit error rate of 2$\times $10−12is measured over a 36-dB loss channel—at least an order-of-magnitude reduction compared to the reference DSP. Power is reduced by 0.33 pJ/b. DSP gate area is reduced by 30%. Noise tolerance is improved by 0.2-mVRMS. Error-free operation is demonstrated on an RS(544,514) KP4 forward error correction (FEC)-encoded link even when the DFE tap values are manually stressed. Techniques for further reduction in complexity are described.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.001 | 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 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".