Generic framework for multi-objective design space exploration for dynamically reconfigurable systems
Bibliographic record
Abstract
In recent years the Run-Time-Reconfigurable (RTR) computing systems have become the core of next generation of adaptive embedded systems. One of the major problems in this class of systems is run-time adaptation of their architecture to the dynamic workload and environmental conditions. In most cases this adaptation is considered as multi-objective optimization process which should be conducted in run-time. Therefore, the goal of this research work was to explore the existing methods of doing multi-objective optimization and analyze their applicability for a system with potential of reconfiguration (i.e. a situation when constrains of the system can change during the course of operation). Then the development of generic framework of this optimization mechanism has to be done. This required analysis and selection of proper approach for multi-objective space exploration. The methodology based on Architecture Configuration Graph was chosen and its searching technique improved to allow faster convergence to a solution that satisfies objective constraints while optimizing specified objective. The run-time complexity analysis was done for modified methodology as well as the testing of the implemented framework to demonstrate its faster performance. The experimental results have shown the ability for run-time architecture adaptation and further utilization of the proposed framework as a core of real-time operating systems (RTOS) for dynamically reconfigurable computers.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".