to Incremental Routing for ECO Applications in FPGAs
Bibliographic record
Abstract
this article, we develop a complete incremental routing methodology for FPGAs using a novel approach called bump and refit (B&R). The basic B&R idea (which was originally proposed in Dutt et al. [1999] in the much simpler context of extending some nets by a segment for the purpose of fault tolerance) in our algorithms is to rearrange some portions of some existing nets on other tracks within their current channels in order to find valid routings for the new/modified nets without requiring any extra routing resources and with little effect on the electrical properties of existing nets. Here we significantly extend the B&R concept to global and detailed incremental routing for FPGAs with complex switchboxes (SBox's) such as those in Lucent's ORCA and Xilinx's Virtex series. We introduce new concepts such as a B&R cost in global routing and the optimal subnet set to relocate for each bumped net (determined using an efficient dynamic programming formulation). We developed optimal and nearoptimal algorithms (called Subsec B&R and Subnet B&R, respectively) to find incremental routing solutions using the B&R paradigm in complex FPGAs (e.g., Lucent's ORCA FPGA) with i-to- j SBox's, as well as an optimal version Fullnet B&R for the VPR architecture from the University of Toronto using the simpler i-to-i SBox's. We compared our algorithms (simply called B&R when no distinction needs to be made between our versions) to two recent incremental routing techniques, Standard (Std) and Rip-up&Reroute (R&R), and to Lucent's A PAR routing tool and the University of Toronto's VPR router used in complete rerouting modes. Experimental results for the ORCA show that B&R is 10 to 20 times faster than complete rerouting using A PAR, and that B&R is also nearly 27% faster and yields new nets w...
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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 | 0.000 |
| 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) | 0.007 | 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".