Doppelgänger
Bibliographic record
Abstract
Modern processors contain large last level caches (LLCs) that consume substantial energy and area yet are imperative for high performance. Cache designs have improved dramatically by considering reference locality. Data values are also a source of optimization. Compression and deduplication exploit data values to use cache storage more efficiently resulting in smaller caches without sacrificing performance. In multi-megabyte LLCs, many identical or similar values may be cached across multiple blocks simultaneously. This redundancy effectively wastes cache capacity. We observe that a large fraction of cache values exhibit approximate similarity. More specifically, values across cache blocks are not identical but are similar. Coupled with approximate computing which observes that some applications can tolerate error or inexactness, we leverage approximate similarity to design a novel LLC architecture: the Doppelgänger cache. The Doppelgänger cache associates the tags of multiple similar blocks with a single data array entry to reduce the amount of data stored. Our design achieves 1.55×, 2.55× and 1.41× reductions in LLC area, dynamic energy and leakage energy without harming performance nor incurring high application error.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.009 |
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".