Low-Complexity Scalable Architectures for Parallel Computation of Similarity Measures
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Processor array architectures have been employed, as an accelerator, to compute similarity distance found in a variety of data mining algorithms. However, most of the proposed architectures in the existing literature are designed in an ad hoc manner without taking into consideration the size and dimensionality of the datasets. Furthermore, data dependencies have not been analyzed, and often, only one design choice is considered for the scheduling and mapping of computational tasks. In this work, we present a systematic methodology to design scalable and area-efficient linear (1-D) processor arrays for the computation of similarity distance matrices. Six possible design options are obtained and analyzed in terms of area and time complexities. The obtained architectures provide us with the flexibility to choose the one that meets hardware constraints for a specific problem size. Comparisons with the previously reported architectures demonstrate that one of the proposed architectures achieves less area and area-delay product besides its scalability to high-dimensional data.
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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 | 0.001 | 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 | 0.001 | 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