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 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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".