The effect of LUT and cluster size on deep-submicron FPGA performance and density
Bibliographic record
Abstract
In this paper, we revisit the field-programmable gate-array (FPGA) architectural issue of the effect of logic block functionality on FPGA performance and density. In particular, in the context of lookup table, cluster-based island-style FPGAs (Betz et al. 1997) we look at the effect of lookup table (LUT) size and cluster size (number of LUTs per cluster) on the speed and logic density of an FPGA. We use a fully timing-driven experimental flow (Betz et al. 1997), (Marquardt, 1999) in which a set of benchmark circuits are synthesized into different cluster-based (Betz and Rose, 1997, 1998) and (Marquardt, 1999) logic block architectures, which contain groups of LUTs and flip-flops. Across all architectures with LUT sizes in the range of 2 to 7 inputs, and cluster size from 1 to 10 LUTs, we have experimentally determined the relationship between the number of inputs required for a cluster as a function of the LUT size (K) and cluster size (N). Second, contrary to previous results, we have shown that clustering small LUTs (sizes 2 and 3) produces better area results than what was presented in the past. However, our results also show that the performance of FPGAs with these small LUT sizes is significantly worse (by almost a factor of 2) than larger LUTs. Hence, as measured by area-delay product, or by performance, these would be a bad choice. Also, we have discovered that LUT sizes of 5 and 6 produce much better area results than were previously believed. Finally, our results show that a LUT size of 4 to 6 and cluster size of between 3-10 provides the best area-delay product for an FPGA.
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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 | 0.001 | 0.006 |
| 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 | 0.000 |
| 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".