Bibliographic record
Abstract
As FPGA capacity increases, a growing challenge is connecting ever-more components with the current low-level FPGA interconnect while keeping designers productive and on-chip communication efficient. We propose augmenting FPGAs with networks-on-chip (NoCs) to simplify design, and we show that this can be done while maintaining or even improving silicon efficiency. We compare the area and speed efficiency of each NoC component when implemented hard versus soft to explore the space and inform our design choices. We then build on this component-level analysis to architect hard NoCs and integrate them into the FPGA fabric; these NoCs are on average 20--23× smaller and 5--6× faster than soft NoCs. A 64-node hard NoC uses only ∼2% of an FPGA's silicon area and metallization. We introduce a new communication efficiency metric: silicon area required per realized communication bandwidth. Soft NoCs consume 4960 mm 2 /TBps, but hard NoCs are 84× more efficient at 59 mm 2 /TBps. Informed design can further reduce the area overhead of NoCs to 23 mm 2 /TBps, which is only 2.6× less efficient than the simplest point-to-point soft links (9 mm 2 /TBps). Despite this almost comparable efficiency, NoCs can switch data across the entire FPGA while point-to-point links are very limited in capability; therefore, hard NoCs are expected to improve FPGA efficiency for more complex styles of communication.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.044 | 0.011 |
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".