Bibliographic record
Abstract
In this paper, the author proposes a new partitioning method for look-up table (LUT)-based field programmable gate arrays (FPGAs). Due to the fixed size of the programmable blocks in an FPGA, partitioning a circuit into sub-circuits with appropriate number of inputs can achieve excellent implementation efficiency. The typical EDA tools deal mainly with AND/OR expressions and therefore are quite inefficient for the parity prediction functions since parity prediction function is inherently AND/XOR in nature. The author examines the application of the Davio's expansion theorem in the decomposition of the AND/XOR expressions. The MCNC benchmark circuits are used here to demonstrate the effectiveness of the proposed techniques. The proposed Davio approach takes only on average 2.75 extra CLBs or 20% of the original area and maximum combinational path delay is reduced by 56.7% compared to the typical method.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".