SoC self-integration mechanism for dynamic reconfigurable systems based on collaborative macro-function units
Bibliographic record
Abstract
For most multi-modal stream processing tasks Dynamic Reconfigurable Systems-on-Chip (SoC) have demonstrated high efficiency in cost and power. These systems utilize partial reconfiguration for dynamic adaptation to changes in the workload or in environment. Mostly, reconfiguration mechanisms are based on central resource management sub-systems deployed in a CPU-core. In this paper we propose a novel mechanism for SoC self-integration based on Collaborative Macro-Function Units (CMFU). Each CMFU consist of a function-specific IP-core combined with a Co-op unit. Co-op units allow CMFUs to co-operate with each other and provide run-time self-integration into the SoC. As well, they provide self-initiation, self-termination and self-synchronization procedures without any central control. This allows a dramatic increase in SoC flexibility, reliability and, finally, survivability. It is specifically important to mitigate hardware faults caused by radiation effects or aging of the die. The proposed mechanism was implemented and tested on a Xilinx Kintex-7 FPGA platform. It was shown that CMFUs can perform self-integration and relocation inside the FPGA almost seamlessly. The hardware overhead of the Co-op unit was relatively small (less than 10% of the entire CMFUs).
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".