Efficient Dynamic Virtual Channel Organization and Architecture for NoC Systems
Bibliographic record
Abstract
A growing number of processing cores on a chip require an efficient and scalable communication structure such as network on chip (NoC). The channel buffer organization of NoC uses virtual channels (VCs) to improve data flow and performance of the NoC system. Dynamically allocated multiqueues (DAMQs) are an effective mechanism to achieve VC flow control with maximum buffer utilization. In this model, VCs employ variable number of buffer slots depending on the traffic. Despite the performance merits of DAMQs, it has some limitations. We propose a new input-port microarchitecture to support our efficient dynamic VC (EDVC) approach that is built on DAMQ buffers. To demonstrate the advantages of EDVC, we compare its microarchitecture with that of the conventional dynamic VC (CDVC), which also employs link-list tables for buffer organization. In terms of hardware, EDVC input-port organization consumes on average 61% less power for application-specific integrated circuit design when compared with the CDVC input port. The saving is even better when compared with VC regulator methodology. An EDVC approach can improve NoC latency by 48%-50% and throughput by 100% on average as compared with the CDVC mechanism.
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.
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 | 0.000 |
| 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".