Using Lazy Instruction Prediction to Reduce Processor Wakeup Power Dissipation
Bibliographic record
Abstract
We study lazy instructions. We define lazy instructions as those spending long periods in the issue queue. Moreover, we investigate lazy instruction predictability and show how their behavior could be exploited to reduce activity and power dissipation in modern processors. We show that a simple and small 64-entry table can identify up to a maximum of 50% of lazy instructions by storing their past behavior. We exploit this to a) reduce wakeup activity and power dissipation in the issue queue and b) reduce the number of in-flight instructions and the average instruction issue delay in the processor. We also introduce two power optimization techniques that use lazy instruction behavior to improve energy efficiency in the processor. Our study shows that, by using these optimizations, it is possible to reduce wakeup activity and power dissipation by up to 34 % and 29 % respectively. This comes with a performance cost of 1.5%. In addition, we reduce average instruction issue delay and the number of inflight instructions by up to 8.5 % and 7 % respectively with no performance cost. 1.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".