A VLSI parallel architecture of a piecewise linear neural network for nonlinear channel equalization
Bibliographic record
Abstract
This paper proposes a systolic architecture based on a multilayer neural network (MNN) to solve the problem of the nonlinear channel equalization. This architecture is based on a piecewise linear multilayer neural network (PL-MNN) algorithm derived from a recursive version (PL-RNN). In place of a sigmoid function, both algorithms use a canonical piecewise linear function, which makes the MNN more suitable for a digital VLSI implementation. The PL-MNN algorithm is more suitable for a VLSI pipelined implementation. The pipeline technique is applied to obtain a high throughput circuit which can be used in a high speed adaptive channel equalization. A performance study on both linear and nonlinear channels is presented. A comparison of results obtained with two conventional methods (LMS and RLS) and the PL-RNN algorithm is presented, and a performance evaluation of the systolic architecture is carried out for a 0.5 /spl mu/m CMOS technology.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".