Stream-Aware Intelligent Memory Controller through HW/SW Co-Design
Bibliographic record
Abstract
Memory hierarchy often represents a significant performance bottleneck in modern computing systems. A promising direction to mitigate this bottleneck is through HW/SW coordination at the system level. However, many existing solutions require changes to legacy programming paradigms, such as ISA extensions, and often provide specialized optimizations limited to specific modules or policies within the memory hierarchy. In this work, we introduce InterStellar, a HW/SW co-design methodology that overcomes these limitations. InterStellar enables the design of a stream-aware memory controller that dynamically adapts its scheduling and memory management policies while proactively batching future stream accesses from off-chip memory. The design is optimized not only for performance, but also for energy efficiency and bandwidth utilization. On systems with eight RISC-V cores, InterStellar achieves significant end-to-end speedup compared to a commercial off-the-shelf (COTS) memory controller: up to 2.72 × for PolyBench, 1.84 × for HPCG, 1.24 × for Rodinia, 1.47 × for Parboil, and 1.29 × for the Phoenix suite.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".