Bibliographic record
Abstract
The increased demand for on-chip communication bandwidth as a result of the multi-core trend has made networks on-chip (NoCs) a compelling choice for the communication backbone in next-generation systems [3]. However, NoC designs have many power, area, and performance trade-offs in topology, buffer sizes, routing algorithms and flow control mechanisms---hence the study of new NoC designs can be very time-intensive. To address this challenge 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 [8, 6], 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: (i) since FPGA implementation is decoupled it can simulate any NoC; and (ii) any NoC can be mapped to the engine without resynthe-sizing it, which can take time for a large FPGA design. We demonstrate that an implementation of DART can achieve over 100x speedup relative to a cycle-based software simulator, while maintaining the same level of simulation accuracy.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.268 | 0.177 |
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".