On the Off-chip Memory Latency of Real-Time Systems: Is DDR DRAM Really\n the Best Option?
Bibliographic record
Abstract
Predictable execution time upon accessing shared memories in multi-core\nreal-time systems is a stringent requirement. A plethora of existing works\nfocus on the analysis of Double Data Rate Dynamic Random Access Memories (DDR\nDRAMs), or redesigning its memory to provide predictable memory behavior. In\nthis paper, we show that DDR DRAMs by construction suffer inherent limitations\nassociated with achieving such predictability. These limitations lead to 1)\nhighly variable access latencies that fluctuate based on various factors such\nas access patterns and memory state from previous accesses, and 2) overly\npessimistic latency bounds. As a result, DDR DRAMs can be ill-suited for some\nreal-time systems that mandate a strict predictable performance with tight\ntiming constraints. Targeting these systems, we promote an alternative off-chip\nmemory solution that is based on the emerging Reduced Latency DRAM (RLDRAM)\nprotocol, and propose a predictable memory controller (RLDC) managing accesses\nto this memory. Comparing with the state-of-the-art predictable DDR\ncontrollers, the proposed solution provides up to 11x less timing variability\nand 6.4x reduction in the worst case memory latency.\n
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".