Cherry-Picking: Exploiting Process Variations in Dark-Silicon Homogeneous Chip Multi-Processors
Bibliographic record
Abstract
It is projected that increasing on-chip integration with technology scaling will lead to the so-called dark silicon era in which more transistors are available on a chip than can be simultaneously powered on. It is conventionally assumed that the dark silicon will be provisioned with heterogeneous resources, for example dedicated hardware accelerators. In this paper we challenge the conventional assumption and build a case for homogeneous dark silicon CMPs that exploit the inherent variations in process parameters that exist in scaled technologies to offer increased performance. Since process variations result in core-to-core variations in power and frequency, the idea is to cherry pick the best subset of cores for an application so as to maximize performance within the power budget. To this end, we propose a polynomial time algorithm for optimal core selection, thread mapping and frequency assignment for a large class of multi-threaded applications. Our experimental results based on the Sniper multi-core simulator show that up to 22% and 30% performance improvement is observed for homogeneous CMPs with 33% and 50% dark silicon, 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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".