A Machine Learning Based Load Value Approximator Guided by the Tightened Value Locality
Bibliographic record
Abstract
This paper addresses two essential memory bottlenecks: 1) memory wall, and 2) bandwidth wall. To accomplish this objective, we propose a machine learning (ML) based model that estimates the values to be loaded from the memory by a wide range of error-resilient applications. The proposed model exploits the feature of tightened value locality, which consists of a periodic load of few unique values. The proposed ML-based load value approximator (LVA) requires minimal overhead as it relies on a hash that encodes the history of events, e.g., history of accessed addresses, and values that can be extracted from the load instruction to be approximated. The proposed LVA completely eliminates memory accesses, i.e., 100% of accesses, in runtime and thus addresses the issue of memory wall and bandwidth wall. Compared to related work, our LVA delivers a maximum accuracy of 95.16% while offering a higher reduction in memory accesses.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".