Evaluating fault tolerance on asymmetric multicore systems‐on‐chip using iso‐metrics
Bibliographic record
Abstract
The end of Dennard scaling has promoted low power consumption into a first‐order concern for computing systems. However, conventional power conservation schemes such as voltage and frequency scaling are reaching their limits when used in performance‐constrained environments. New technologies are required to break the power wall while sustaining performance on future processors. Low‐power embedded processors and near‐threshold voltage computing (NTVC) have been proposed as viable solutions to tackle the power wall in future computing systems. Unfortunately, these technologies may also compromise per‐core performance and, in the case of NTVC, reliability. These limitations would make them unsuitable for HPC systems and datacenters. To demonstrate that emerging low‐power processing technologies can effectively replace conventional technologies, this study relies on ARM's big.LITTLE processors as both an actual and emulation platform, and state‐of‐the‐art implementations of the CG solver. For NTVC in particular, the study describes how efficient algorithm‐based fault tolerance schemes preserve the power and energy benefits of very low voltage operation.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".