Dynamic Virtual Channel Configuration for Efficient Multicore Systems
Bibliographic record
Abstract
With the growing number of on-chip cores in multicore systems, there is an urgent need of efficient communication structures. NoC (Network-on-Chip) plays an important role in determining the performance of on-chip communication for multicore systems. Specifically, packet-based wormhole communication is known as the most viable solution for an MPSoC involving NoCs. In NoC design, the buffer organization facilitates the use of Virtual Channels (VC). Formally, a VC organization can be categorized into two types: static and dynamic. In dynamic and adaptive VC organization, variable number of buffer slots is used for VCs depending on the real-time on-chip traffic conditions. In this context, we introduce a new dynamic scheme for Efficient Virtual Channel organization (EVC), where a VC is reserved when a message packet enters the router and released when the packet leaves the router. This prevents a VC to hold more than one packet that subsequently removes the blocking of other packets. Our proposed technique can be implemented by amending the previously introduced dynamically allocated multi-queue schemes. In these schemes, an input or output port comprises of a centralized buffer whose slots are dynamically allocated to VCs according to real-time traffic. The simulation results support the advantages of our proposed EVC methodology and the experimental results confirm that our approach improves network latency and throughput as compared to conventional VC based multicore system designs.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".