K-means implementation on FPGA for high-dimensional data using triangle inequality
Bibliographic record
Abstract
One of the challenges to data mining raised by technology development is that both data size and dimensionality is growing rapidly. K-means, one of the most popular clustering algorithms in data mining, suffers in computational time when used for large data sets and data with high dimensionality. In this paper, we propose a hardware architecture for K-means with triangle inequality optimization on FPGA. An optimal 8-bit square calculator for 6-LUT architectures is described to minimize the hardware cost and an approximation solution is proposed to avoid square root calculation in the original triangle inequality optimization. Our software and hardware experiments are tested with the MNIST benchmark and uniform random numbers of various size. This approximation results in 2% more distance calculations for MNIST and 5% for uniform random numbers than the original optimization. Compared to the baseline hardware system without optimization, our approach achieves up to 77% improvement in processing time with about 10% logic overhead. We demonstrate that the hardware can achieve 55-fold speed up compared to software for the 1024 MNIST.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".