Un/DoPack: Re-Clustering of Large System-on-Chip Designs with Interconnect Variation for Low-Cost FPGAs
Bibliographic record
Abstract
.FPGA device area is dominated by interconnect, so low-cost FPGA architectures often have reduced interconnect capacity. This limited routing capacity creates a hard channel width constraint that can make it difficult for CAD tools to successfully map a circuit into these devices. Instead of migrating a design to a high-cost, resource-rich architecture that is easier to route, we present a cheaper alternative: a fully automated CAD flow (Un/DoPack) that finds local regions of high interconnect demand and reduces it by spreading out the logic in that region. This is done by introducing whitespace in the form of empty logic elements (LEs) within the configurable logic blocks (CLBs) of the congested region. After spreading, the congested region occupies more routing channels and so obtains access to greater aggregate interconnect capacity. Although this has the side effect of using more CLBs, it has the advantage of lowering peak interconnect demands and making a previously-unroutable circuit routable. We also design a new set of synthetic benchmark circuits that model interconnect variation within a large design. Using these benchmarks, we show that circuits with high interconnect variation require FPGA devices to have large channel widths. However, since congestion of such circuits is localized, Un/DoPack is very good at reducing the peak demands of circuits with high interconnect variation. Our results suggest that even for an average Rent exponent of 0.62 (a modest value), a large variation of this exponent within a design will also require FPGAs to have large channel widths. Thus, it is crucial to study interconnect variation of benchmark circuits when designing low-cost FPGAs. Previous research studying interconnect properties focuses on average Rent exponent values of each design, but we believe new work should study variation as well. For circuits with high interconnect variation, we demonstrate that channel widths can be reduced by up to ~40% with only ~10% increase in area
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".