Improving Cost-Effectiveness Using a Micro-level Static Architecture for Stream Applications
Bibliographic record
Abstract
A high-speed visual processing system often requires real-time algorithm adaptation because of environmental changes, user requests or multi-object processing standards. The reconfigurable platform based on a run-time reconfigurable FPGA can employ dynamic algorithm adaptation if the reconfiguration overhead stays within the application's temporal redundancy. We propose the system that ensembles an application specific micro-level static architecture on the reconfigurable device to provide the framework used by run-time reconfigurable procedures. The idea of the proposed system is employed under the visual process of an autonomous satellite docking system. The class of algorithms targeted for the system consists of stereo rectification, stereo extraction and object tracking. Due to the high speed requirements (e.g. 200 fps) of an extraterrestrial docking system to respond and grasp a moving target, algorithmic adaptation via dynamic reconfiguration should be conducted within the nominal response of a frame (i.e. 5 ms). We discuss how our system improves the cost-effectiveness for a given application.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".