Binary Insertion Sort for Hardware Acceleration: Balancing Flexibility and Resource Efficiency
Bibliographic record
Abstract
This paper presents Binary Insertion Sort (BIS), a parallel-friendly sorting algorithm optimized for hardware implementation. By enabling up to$k=\left\lceil\log _{2}(N-1)\right\rceil$concurrent binary searches, BIS targets a theoretical time complexity of$\mathcal{O}(N)$, offering an efficient alternative to comparator-intensive designs. Comparative evaluations against state-of-the-art hard-ware sorting methods, including DL Sort and OWS, show that BIS reduces comparison operations by up to 50% relative to DL Sort while requiring only$\mathcal{O}(\log N)$comparators, significantly lowering hardware complexity. Although OWS achieves minimal data movement, its strict dependence on bit-width and input size limits its general applicability. BIS, by contrast, provides a more flexible and scalable architecture, balancing performance, resource efficiency, and adaptability. These qualities position BIS as a strong candidate for integration in real-time and resource-constrained systems.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".