Bibliographic record
Abstract
Long distance interconnect delays are not scaling well with process technology, thereby leading to long routes strongly impacting the critical path of large FPGA designs. This forces the designer to pipeline long connections, which necessitates time consuming logic redesign in traditional latency-sensitive systems. Latency-insensitive design (LID) is an increasingly attractive alternative as the typical latency of long distance interconnect grows, since LID decouples the design of the interconnect from that of the computational modules. By doing so, LID simplifies timing closure, improves forward compatibility (migration of systems to future FPGAs) and makes automated system-level pipelining feasible. Modern FPGAs, such as Stratix 10 which includes pipelined interconnect, make it difficult to use traditional LID solutions without significant area and frequency overhead. We present two LID styles that are more suitable for FPGAs and compare them to traditional LID. Our best system gained 2x area efficiency and 18% speed efficiency over traditional LID. Additionally, our designs come at a minimal speed overhead of only 3% compared to that of a latency-sensitive design.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".