Asymmetric-frequency clustering: a power-aware back-end for high-performance processors
Bibliographic record
Abstract
We introduce asymmetric frequency clustering (AFC), a micro-architectural technique that reduces the dynamic power dissipated by a processor's back-end while maintaining high performance. We present a dual-cluster, dual-frequency machine comprising a performance oriented cluster and a power-aware one. The power-aware cluster operates at half the frequency of the performance oriented cluster and uses a lower voltage supply. We show that this organization significantly reduces back-end power dissipation by executing non-performance-critical instructions in the power-aware cluster. AFC localizes the two frequency/voltage domains. Consequently, it mitigates many of the complexities associated with maintaining multiple supply voltage and frequency domains on the same chip. Key to the success of this technique are methods that assign as many instructions as possible to the slower/lower power cluster without impacting overall performance. We evaluate our techniques using a subset of SPEC2000 and SPEC95. AFC provides a 16% back-end power reduction with 1.5% performance loss compared to a conventional, dual-clustered processor where each cluster has schedulers of the same width and length.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".