Lighting the Dark-Silicon 3D Chip Multi-processors by Exploiting Heterogeneity in Cache Hierarchy
Bibliographic record
Abstract
This paper addresses a set of design paradigms by exploiting device and architectural heterogeneity to mitigate the dark silicon. We exploit Non-Volatile Memory (NVM) as potential replacements to conventional caches. Also, we study the problem of dynamic thread mapping in future Chip Multi-Processors (CMPs) via an efficient scheduler. Evaluations on a 3D architecture consisting of 8 core (a big and a small core on each tile) show that the proposed method provides up to 7% average performance improvement for multithreaded benchmarks, and 9% for multiprogrammed workloads. The results also show 62.5% and 67.7% on average energy-delay product (EDP) improvement for multithreaded and multiprogrammed workloads respectively, with 7.87% area overhead compared to the conventional methods.
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How this classification was reachedexpand
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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".