Bibliographic record
Abstract
This paper proposes a novel method for efficient realization of parity prediction functions in FPGAs. Improving FPGA efficiency can improve the cost/logic ratio, which may allow FPGAs to be used in more application areas. Using ESOP (exclusive-sum-of-products) to represent logic functions often requires fewer product terms than traditional SOP (sum-of-products) methods. Since FPGA logic functions are implemented based on the number of inputs required, rather than complexity of gates, reducing the number of product terms/literals can produce savings. Commercial EDA tools are suboptimal when it comes to synthesizing logic functions into FPGAs. Our results show that our algorithm improves both area and performance metrics. It is believed that our algorithm will also improve FPGA efficiency in implementing arithmetic circuits, error correcting/detecting circuits, and any other XOR-intensive function. Our proposed method, ExorBDS, uses a stage of ESOP minimization, followed by a stage of decomposition using binary decision diagrams (BDDs). Experiments were conducted on 14 MCNC benchmark circuits. The combination of ESOP minimization and BDD-based decomposition showed superior results to that of just using ESOP minimization or BDD-based decomposition in isolation. The results, when compared with commercial EDA tools, are encouraging. On average, our method uses 36.85% of the number of LUTs (look-up tables), has 87.11% of the maximum combinational path delay, and has 26.16% of the area-delay product.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.006 | 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".