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 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".