Reduction design for generic universal switch blocks
Bibliographic record
Abstract
A k -side switch block with W terminals per side is said to be a universal switch block (( k , W )-USB) if every set of the nets satisfying the routing constraint (i.e., the number of nets on each side is at most W ) is simultaneously routable through the switch block. The (4, W )-USB was originated by designing better switch modules for 2-D FPGAs, such as Xilinx XC4000-type FPGAs, whereas the generic USBs can be applied in multidimensional or some nonconventional 2-D FPGA architectures. The problem we study in this article is to design ( k , W )-USBs with the minimum number of switches for any given pair of ( k , W ). We provide graph models for routing requirements and switch blocks and develop a series of decomposition theorems for routing requirements with the help of a new graph model. The powerful decomposition theory leads to the automatic generation of routing requirements and a detailed routing algorithm, as well as the reduction design method of building large USBs by smaller ones. As a result, we derive a class of well-structured and highly scalable optimum ( k , W )-USBs for k ≤ 6, or even W s, and near-optimum ( k , W )-USBs for k ≥ 7 and odd W s. We also give routing experiments to justify the routing improvement upon the entire chip using the USBs. The results demonstrate the usefulness of USBs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".