Macro-Programmable Reconfigurable Stream Processor for Collaborative Multi-Agent Systems
Bibliographic record
Abstract
Growing demand for high speed processing of streamed data (e.g. video-streams, digital signal streams, communication streams, etc.) in the advanced manufacturing environments requires the adequate cost-efficient stream-processing platforms. Platforms based on the embedded microprocessors often cannot satisfy performance requirements due to limitations associated with the sequential nature of data execution process. During the last decade, development and prototyping of the above embedded platforms has started moving towards utilization of the Field Programmable Gate Array (FPGA) devices. However, the programming of an application to the FPGA based platform became an issue due to relatively complicated hardware design process. The paper presents an approach which allows simplification of the application programming process by utilization of: (i) the uniformed FPGA platform with the dynamically reconfigurable architecture, (ii) a programming technique based on a temporal partitioning of the application in segments which can be described in terms of macro-operators (function specific virtual components). The paper describes the concept of the approach, presents the analytical investigation and experimental verification of the cost-effectiveness of the proposed platform comparing to the platforms based on sequential micro-processors. It is also shown that the approach can be beneficially utilized in collaborative design and manufacturing.
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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 | 0.000 |
| 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) | 0.002 | 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".