Implementation Issues of Kohonen Self-Organizing Map Realized on FPGA
Bibliographic record
Abstract
Presented are the investigations showing an impact of the length of data signals in hardware implemented Kohonen Self-Organizing Maps (SOM) on the quality of the learning process. The aim of this work was to determine the allowable reduction of the number of bits in particu- lar signals that does not deteriorate the network behavior. The eciency of the learning process has been quantied by using the quantization error. The results obtained for the SOM realized on Field Programmable Gate Array (FPGA), as well as by means of the software model of the SOM show that the smallest allowable resolution (expressed in bits) of the weight sig- nals equals seven, while the minimal bit length of the neighborhood signal ranges from 3 to 6 (depending on the map topology). For such values and properly selected values of other parameters the learning process remains undisturbed. Reducing the number of bits has an inuence on the number of neurons that can be synthesized on a single FPGA device.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".