Logic block clustering of large designs for channel-width constrained FPGAs
Bibliographic record
Abstract
In this paper we present a system level technique for mapping large, multiple-IP-block designs to channel-width constrained FPGAs. Most FPGA clustering tools [2, 3, 11] aim to reduce the amount of inter-cluster connections, hence reducing channel width needs. However, if this exceeds the FPGA's channel width (a hard constraint), then the circuit still cannot be routed. Previous work [11, 12] depopulates logic clusters (CLBs) to reduce channel width. By depopulating non-uniformly, i.e. depopulate more in hard-to-route regions, we show a graceful trade-off between channel width and CLB count. This makes it possible to target specific channel-width constraints during clustering with minimal CLB inflation. Results show channel width decreases of up to 20% with a 5% increase in area. Further decreases of nearly 50% are possible at 3.3 times the original area. Despite the area increase, this technique creates routable solutions from otherwise-unroutable circuits.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".