Locality management using multiple SPMs on the Multi-Level Computing Architecture
Bibliographic record
Abstract
The multi-level computing architecture (MLCA) is a novel system-on-chip architecture for embedded systems designed to exploit task-level and instruction-level parallelism in multimedia applications. The MLCA provides a unique two-level programming model that simplifies the development of embedded applications. To cope with increasing intra-system communication delays, we introduce a distributed memory version of the MLCA where separate storage is used for global and local application data. Global data is stored on multiple on-chip scratch-pad memories (SPMs) with non-uniform-memory access (NUMA) latencies, while local data is stored on PU-private memories. In such designs, one of the key factors affecting application performance is the locality of access to global data. We introduce programming constructs and run-time support to dynamically manage data stored in the SPMs and to influence run-time task scheduling. Collectively, our techniques improve performance by 6%-40%, compared to simple static memory management and scheduling approaches
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".