Double-Pumping the Interconnect for Area Reduction in Coarse-Grained Reconfigurable Arrays
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Bibliographic record
Abstract
We consider double-pumped interconnect as a means of area reduction in coarse-grained reconfigurable arrays (CGRAs). Interconnect multiplexers comprise a considerable portion of CGRA area. We apply double-pumping to halve the word-width of the interconnect multiplexers, saving area. The interconnect is operated at twice the system clock frequency, where the top and bottom half-words of a value are communicated in the first and second half of a clock cycle. Several circuit-level approaches for double-pumping are considered, and evaluated in different CGRA architectures with varied interconnect richness. Area and performance consequences are assessed through a 45nm standard-cell ASIC implementation. Overall CGRA area improvements of up to 16% are observed, depending on the CGRA architecture and double-pumping implementation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it