An Efficient Evolutionary Task Scheduling/Binding Framework for Reconfigurable Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Several embedded application domains for reconfigurable systems tend to combine frequent changes with high performance demands of their workloads such as image processing, wearable computing, and network processors. Time multiplexing of reconfigurable hardware resources raises a number of new issues, ranging from run-time systems to complex programming models that usually form a reconfigurable operating system (ROS). In this paper, an efficient ROS framework that aids the designer from the early design stages all the way to the actual hardware implementation is proposed and implemented. An efficient reconfigurable platform is implemented along with novel placement/scheduling algorithms. The proposed algorithms tend to reuse hardware tasks to reduce reconfiguration overhead, migrate tasks between software and hardware to efficiently utilize resources, and reduce computation time. A supporting framework for efficient mapping of execution units to task graphs in a run-time reconfigurable system is also designed. The framework utilizes an Island Based Genetic Algorithm flow that optimizes several objectives including performance, area, and power consumption. The proposed Island Based GA framework achieves on average 55.2% improvement over a single-GA implementation and an 80.7% improvement over a baseline random allocation and binding approach.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it