Achieving Flexible Global Reconfiguration in NoCs Using Reconfigurable Rings
Bibliographic record
Abstract
The communication behaviors in NoCs of chip-multiprocessors exhibit great spatial and temporal variations, which introduce significant challenges for the reconfiguration in NoCs. Existing reconfigurable NoCs are still far from ideal reconfiguration scenarios, in which globally reconfigurable interconnects can be immediately reconfigured to provide bandwidths on demand for varying traffic flows. In this paper, we propose a hybrid NoC architecture that globally reconfigures the ring-based interconnect to adapt to the varying traffic flows with a high flexibility. The ring-based interconnect has the following advantages. First, it includes horizontal rings and vertical rings, which can be dynamically combined or split to provide low-latency channels for heavy traffic flows. Second, each combined ring connects a number of nodes, thereby improving both the utilization of each ring and the probability to reuse previous reconfigurable interconnects. Finally, the reconfiguration algorithm has a linear-time complexity and can be implemented using a low-overhead hardware design, making it possible to achieve a fast reconfiguration in NoCs. The experimental results show that compared to recent reconfigurable NoCs, the proposed NoC architecture can greatly improve the saturation throughput for synthetic traffic patterns, and reduce the packet latency over 40 percent for realistic benchmarks without incurring significant area and power overhead.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".