Fundamental Latency Trade-off in Architecting DRAM Caches: Outperforming Impractical SRAM-Tags with a Simple and Practical Design
Bibliographic record
Abstract
This paper analyzes the design trade-offs in architecting large-scale DRAM caches. Prior research, including the recent work from Loh and Hill, have organized DRAM caches similar to conventional caches. In this paper, we contend that some of the basic design decisions typically made for conventional caches (such as serialization of tag and data access, large associativity, and update of replacement state) are detrimental to the performance of DRAM caches, as they exacerbate the already high hit latency. We show that higher performance can be obtained by optimizing the DRAM cache architecture first for latency, and then for hit rate. We propose a latency-optimized cache architecture, called Alloy Cache, that eliminates the delay due to tag serialization by streaming tag and data together in a single burst. We also propose a simple and highly effective Memory Access Predictor that incurs a storage overhead of 96 bytes per core and a latency of 1 cycle. It helps service cache misses faster without the need to wait for a cache miss detection in the common case. Our evaluations show that our latency-optimized cache design significantly outperforms both the recent proposal from Loh and Hill, as well as an impractical SRAM Tag-Store design that incurs an unacceptable overhead of several tens of megabytes. On average, the proposal from Loh and Hill provides 8.7% performance improvement, the "idealized" SRAM Tag design provides 24%, and our simple latency-optimized design provides 35%.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".