Bibliographic record
Abstract
Summary form only given. FPGAs are amongst the world's largest and most complex integrated circuits, and they continue to be very early adopters of the latest process technology. This talk will describe some of the driving applications and technology trends pushing FPGAs to 28 nm and smaller process nodes. We will also highlight how FPGA architecture is evolving, as exemplified by Altera's Stratix V FPGAs. Power management and silicon efficiency issues are pushing FPGAs to become somewhat more application-targeted, and to incorporate larger amounts of hard logic that makes them more complete systems-on-achip. In addition, the very high I/O bandwidth requirements of next-generation systems are driving innovation in both high-speed memory interface design and high-speed serial transceiver design. Stratix V supports partial reconfiguration to increase silicon efficiency by swapping in different functionality over time. We will describe both the hardware that enables partial reconfiguration, and the software tools that will enable efficient design without becoming entangled in low-level physical details. Finally, we will discuss both software challenges and promising research efforts to create CAD tools that will help designers productively create the very large systems enabled by modern FPGAs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".