Proceedings of the 2006 ACM/SIGDA 14th international symposium on Field programmable gate arrays
Bibliographic record
Abstract
Welcome to FPGA 2006, the Fourteenth International Symposium on Field-Programmable Gate Arrays. FPGA remains the premier conference for advances in all areas related to FPGA technology. With ever increasing NRE costs, DSM effects, and FPGA capacities, the importance of FPGAs today is as high as ever. Continuing our traditional themes, the papers in this year's symposium present new developments in architecture, the impact of technology on FPGA designs, improved CAD techniques and algorithms, and new techniques for efficiently mapping applications to FPGAs. Papers and the panel discussion demonstrate increasing attention to power and point out where there is still much room for innovation and improvement in FPGA architecture and CAD.This year the symposium attracted one hundred submissions. We have selected 22 papers for presentation. We also invited 30 papers for poster presentation.FPGA 2006 provides a relaxed atmosphere for informal information exchange, networking, and stimulating discussion with leaders in the FPGA field from both industry and academia. Paper sessions are separated by ample time to peruse the poster presentations and discuss the latest developments in the field. We hope you will take this opportunity to see the cutting-edge of FPGA innovation, make new contacts, and reacquaint yourself with old colleagues.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.067 | 0.039 |
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".