Mitigation of variations in environmental conditions by SoPC architecture adaptation
Bibliographic record
Abstract
Run-time reconfigurable computing systems can offer increased flexibility when compared with traditional systems, a feature which can make them attractive for space-borne computing applications. This flexibility can allow a system to adapt to changes in operating conditions, such as reductions in available power, reductions in available resources (wither due to increases in task deployment, or due to permanent faults) or changes in required performance (processing rate). A unified mechanism which allows such adaptations is presented in this paper, based on the concept of architectural variants for a given algorithm; the different architectures exhibit different resource utilization, performance and power consumption attributes. This allows the system to meet various constraints through the judicious selection and deployment of an architecture variant by the appropriate reconfiguration of an implemented System-on Programmable Chip (SoPC). The adaptive capabilities of the proposed mechanism are experimentally tested on the Xilinx Kintex-7 FPGA platform (KC-705) using a video processing application aimed at 720p video streams. Three different versions of the application algorithm are implemented, allowing for performance variations between 3 and 300+ frames/second, while exhibiting a large power consumption range (from 1 mW to 81 mW).
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".