DART: A Programmable Architecture for NoC Simulation on FPGAs
Why this work is in the frame
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Bibliographic record
Abstract
The increased demand for on-chip communication bandwidth as a result of the multicore trend has made packet-switched networks-on-chip (NoCs) a more compelling choice for the communication backbone in next-generation systems[1] . However, NoC designs have many power, area, and performance tradeoffs in topology, buffer sizes, routing algorithms, and flow control mechanisms-hence, the study of new NoC designs can be very time intensive. To address these challenges, we propose DART, a fast and flexible FPGA-based NoC simulation architecture. Rather than laying the NoC out in hardware on the FPGA like previous approaches [2],[3] , our design virtualizes the NoC by mapping its components to a generic NoC simulation engine, composed of a fully connected collection of fundamental components (e.g., routers and flit queues). This approach has two main advantages: 1) since it is virtualized it can simulate any NoC, and 2) any NoC can be mapped to the engine without rebuilding it, which can take significant time for a large FPGA design. We demonstrate 1) that an implementation of DART on a Virtex-II Pro FPGA can achieve over 100 × speedup over the cycle-based software simulator Booksim [4], while maintaining the same level of simulation accuracy, and 2) that a more modern Virtex-6 FPGA can accommodate a 49-node DART implementation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it