Placement Optimization for NoC-Enhanced FPGAs
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Bibliographic record
Abstract
Field-programmable gate array (FPGA) architectures have recently incorporated hardened networks-on-chip (NoCs) to enable more efficient and easier system-level integration. However, the embedding of hard NoCs presents a new challenge for FPGA computer-aided design (CAD); the tools need to optimize the placement of circuit netlist primitives to not only minimize total wirelength and critical path delay, but also consider the NoC traffic patterns between modules to minimize their aggregate bandwidth and/or 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 enhance the placement engine in VPR to co-optimize traditional circuit implementation metrics (e.g. wirelength, critical path delay) and NoC performance metrics (e.g. congestion, bandwidth utilization, latency) when mapping an application design with NoC-attached modules to a candidate NoC-enhanced FPGA architecture. We test our VPR enhancements using a variety of synthetic benchmarks and verify that the placement engine can effectively optimize NoC aggregate bandwidth and meet specified latency constraints. Then, we present a complete flow that integrates VPR with a high-level SystemC architecture simulator, RAD-Sim, that can capture the NoC traffic flows of complete application designs and use it to drive VPR's placement optimizations. We showcase this combined flow using a real application design from the deep learning domain. The results show that our NoC-enhanced VPR flow can result in 2x reduction in NoC aggregate bandwidth (on average) compared to a NoC-agnostic flow, without affecting the design's wirelength or critical path delay.
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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