Design exploration of ASIP architectures for the K-Nearest Neighbor machine-learning algorithm
Why this work is in the frame
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Bibliographic record
Abstract
Increasingly, machine-learning algorithms are playing an important role in the context of embedded and real-time systems. Applications such as wireless sensor networks, security, and commercial enterprises are increasingly relying on machine-learning algorithms to efficiently make predictive decisions based on the large volumes of data these systems collect. Therefore, there is a need to accelerate the runtime of these algorithms, especially for real-time applications. In this paper, we propose several Application Specific Instruction Processor (ASIP) architectures for the K-Nearest Neighbor (KNN) classification algorithm. Each ASIP is developed using Cadence Tensilica tools and represents a tightly-coupled architecture. Our experimental results, based on several benchmarks, show that proposed ASIPs achieve speedups of 86×-650× over the original software implementation.
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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