Predicting the performance of application-specific NoCs implemented on FPGAs
Bibliographic record
Abstract
Modern FPGAs are able to implement complex systems such as Systems-on-Chips (SoCs) and Networks-on-Chips (NoCs). Appropriate NoC topology choices for ASICs have been investigated and typically topologies that can be easily mapped to a two-dimensional fabric are used to reduce chip area and ensure electrical characteristics. However, for FPGAs, each device's size and routing fabric are fixed. Since these resources exist independent of use, the choice of topology is only limited by the performance of the NoC itself. In this work, we investigate how topology characteristics impact a NoC's performance on an FPGA. From this analysis, we have created an analytical model that describes the maximum operating frequency of a NoC as a function of the topology's network parameters. This model is in the form of a simple equation that is accurate to within 4.68% across a range of topologies, chip sizes, and device families. It demonstrates how an FPGA's prefabricated routing interconnect provides increased freedom in the selection of application-specific topologies. Furthermore, it can also be used by designers for topology design space exploration before implementation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 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".