Computational and storage power optimizations for the O-GEHL branch predictor
Bibliographic record
Abstract
In recent years, highly accurate branch predictors have been proposed primarily for high performance processors. Unfortunately such predictors are extremely energy consuming and in some cases not practical as they come with excessive prediction latency. One example of such predictors is the O-GEHL predictor. To achieve high accuracy, O-GEHL relies on large tables and extensive computations and requires high energy and long prediction delay.In this work we propose power optimization techniques that aim at reducing both computational complexity and storage size for the O-GEHL predictor. We show that by eliminating unnecessary data from computations, we can reduce both predictor's energy consumption and delay. Moreover, we apply information theory findings to remove redundant storage, without any significant accuracy penalty. We reduce the dynamic and static power dissipated in the computational parts of the predictor by up to 74% and 65% respectively. Meantime we improve performance by up to 12% as we make faster prediction possible.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".