Hot spare components for performance-cost improvement in multi-core SIMT
Bibliographic record
Abstract
Adding redundant components is a well known technique for replacing defective components either before shipment or in the field, resulting yield improvement and consequently cost reduction. However, most yield improvement strategies utilize redundant components only when another component fails (i.e., cold spares). In this paper, we investigate the cost and performance implications of employing hot spares in multi-core single-instruction, multiple-thread (SIMT) processors. Hot spares are available to increase yield (and reduce costs) when the components are defective; otherwise, they can be used to improve performance in the field. Starting with a baseline architecture with six cores, and 32 lanes each, we added three hot spare cores, with two lanes each. When we make the lanes of the hot spares available to replace defective lanes in the baseline cores, we observe that expected performance per cost improved more than 2.5 and 1.7 times relative to systems integrating no redundancy and cold spares, respectively.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".