Evaluating fault tolerance on asymmetric multicore systems‐on‐chip using iso‐metrics
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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