Design Mapping And Optimization For Field Programmable Gate Arrays With Embedded Networks On Chip
Bibliographic record
Abstract
Field-programmable gate array (FPGA) architectures have recently incorporated hardened networkson-chip (NoCs) to enable more efficient and easier system-level integration. This presents a new challenge for FPGA computer-aided design (CAD); the tools must optimize the placement of netlist primitives to not only minimize wirelength and critical path delay, but also consider the traffic patterns between modules to minimize NoC aggregate bandwidth and meet latency constraints.This work enables flexible modeling of FPGA architectures with hard NoCs in the open-source versatile place & route (VPR) CAD flow, facilitating both CAD and architecture research. We first enhance the VPR placement engine to co-optimize traditional circuit placement metrics and NoC performance metrics. We then validate our enhancements on a variety of synthetic benchmarks.Finally, we evaluate the NoC-optimized placer on real applications and show that our flow results in 2X less NoC aggregate bandwidth compared to an NoC-agonistic flow.
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How this classification was reachedexpand
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".