Hardware implementation of a real-time genetic algorithm for adaptive filtering applications
Why this work is in the frame
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Bibliographic record
Abstract
Genetic algorithms are increasingly being used to address adaptive filtering problems. The interest lies in their ability to find the global solutions for linear and nonlinear problems. However, all the work available in the literature use software implementations running on sequential processors. This work proposes a hardware architecture of a real-time genetic algorithm for adaptive filtering applications. Specifically designed genetic operators are proposed to improve processing performance and robustness to the quantization effect, making low bit-wordlength fixed-point arithmetic implementation possible, which permit hardware cost saving. The proposed architecture is modeled in VHDL and implemented in FPGA using 6-bits wordlength, addressing linear and nonlinear auto regressive moving average (ARMA) model parameters identification problem. The implementation experiments show high signal processing performance and low resources cost.
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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 it