Segment Streaming for the Three-Phase Execution Model: Design and Implementation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Scheduling tasks using the three-phase execution model (load-execute-unload) can effectively reduce the contention on shared resources in real-time systems. Due to system and program constraints, a task is generally segmented and executed over multiple intervals. Several works showed that co-scheduling memory (unload-load) and computation phases can improve the system schedulability by hiding the memory transfer time. However, this is limited to segments of different tasks and hence executing segments of the same task back-to-back is not allowed. In this paper, we propose a new streaming model to allow overlapping the memory and execution phases of segments of the same task. This is accomplished by a segmentation framework implemented within an LLVM-based compiler-level tool along with a Real-Time Operating System (RTOS) API to handle load/unload requests. Memory phases are processed by a DMA engine that loads/unloads the task content into ScratchPad Memory (SPM). We provide a schedulability analysis of the proposed model under fixed priority partitioned scheme and an RTOS implementation of the API on a latest-generation Multiprocessor System-on-Chip (MPSoC).
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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 | 0.001 | 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 | 0.000 | 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 it