On the Off-chip Memory Latency of Real-Time Systems: Is DDR DRAM Really\n the Best Option?
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it