Automatic Topology Optimization for FPGA Interconnect Synthesis
Bibliographic record
Abstract
The goal of FPGA interconnect synthesis is to generate a physical network that connects user-supplied functional modules according to a logical specification of the desired connectivity. In this paper, we augment an existing FPGA interconnect synthesis flow with the ability to automatically design the topology of the generated network while reducing its area subject to user-supplied performance specifications. The key specification is a per-transmission importance value representing the designer's willingness to have a transmission contend with other transmissions. The designer may also optionally specify that certain transmissions will never temporally overlap. We present an iterative algorithm that generates a topology which respects these specifications, with the goal of reducing area. Optimization decisions are guided by pre-characterized area models of interconnect primitives and an analytical worst-case traffic contention model. We apply our approach to a case study of an FPGA-based linear algebra application, where we successfully optimize the topologies of two of its sub-networks resulting in area savings of 60% and 75% with no overall performance degredation.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".