Application of FPGA technology to accelerate the finite-difference time-domain (FDTD) method
Bibliographic record
Abstract
The continuing advances in the field of electrical engineering, in areas like cellular communications, fiber optics, mobile and multi-gigahertz electronics have necessitated a computer-assisted design approach to the complex electromagnetic interactions and problems that arise. Finite-Difference Time-Domain (FDTD) Analysis is a very powerful tool for the modeling of electromagnetic phenomena. The algorithm is computationally intensive and simulations can run for a few hours to several days. Increasing the computation speed and decreasing the run times of this algorithm would bring greater productivity and new avenues of research to many facets of electrical engineering.The algorithm is transferred to custom FPGA-based hardware using a pipelined bit-serial arithmetic architecture. A one-dimensional resonator is used to verify the implementation and explore the hardware speed and costs. The computational speed is extremely fast and is not related to the number of computational cells in the simulation. Finally, a discussion of future research is presented.
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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) | 0.000 | 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) | 0.004 | 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".