Interconnect driver design for long wires in field-programmable gate arrays
Bibliographic record
Abstract
Each new semiconductor technology node brings smaller transistors and wires. Although this makes transistors faster, wires get slower. As FPGA device designers strive to obtain the lowest possible circuit delays from a given technology node, they must take an increasingly interconnect-focused viewpoint in the design process. In particular, for long interconnect wires, signals now require rebuffering somewhere in the middle of the wire. This paper presents a framework for designing and evaluating long, buffered interconnect wires in FPGAs with near-optimal delay performance. Given a target physical wire length, width and spacing, the method determines the number, size, and position of buffers required to obtain the fastest signal velocity for programmable interconnect. A metric introduced during the design is the "path delay profile", or the arrival time of a signal at different points of a long wire. This method is used to design buffering strategies for interconnect based on 0.5mm, 2mm, and 3mm wire lengths in 180nm technology. These interconnect designs are coded into VPR along with an improved timing analyzer which accurately determines the "path delay profile" arrival times. Using VPR, average critical-path delay is reduced by 19% for 0.5mm wires and by up to 46% for 3mm wires over previous designs given in Lemieux et al. (2004)
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".