SoPC Self-Integration Mechanism for Seamless Architecture Adaptation to Stream Workload Variations
Bibliographic record
Abstract
Field-programmable gate arrays are becoming one of the implementation platforms of choice for computationally intensive embedded applications, such as multimode stream processors; such systems often exhibit poor cost-efficiency as various system modules can be idle, based on operating mode. This problem can be addressed through the use of reconfigurable computing, which allows underlying logic resources to be shared among system modules; using this approach, an application and mode specific processor can be generated at run-time. However, this generation process can interfere with application workloads; this is particularly true in the case of high data-rate stream processors. To address this problem, this brief presents a system-on-programmable-chip self-integration mechanism aimed at reconfigurable stream processors. The proposed mechanism is implemented using a distributed architecture and the multimode adaptive collaborative reconfigurable self-organized system framework. The mechanism arranges configuration, link establishment, and scheduling tasks around the stream workload, which allows for seamless run-time architecture adaptation. When compared with traditional approaches, based on central, instruction-based sequential processors, the proposed approach is shown to offer a faster (up to 10 times) link establishment and the scheduling capabilities; more importantly, the proposed mechanism can offer seamless run-time architecture adaptation by allowing the overlap of processing and configuration tasks.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".