Latency-optimised 3D multi-FPGA system with serial optical interface
Bibliographic record
Abstract
Multi-FPGA systems (MFSs) are capable of prototyping large SoCs. However, planar 2D MFSs with electrical interconnections have broader spatial distribution and large off-chip delays. One good solution for this problem is to use a three-dimensional (3D) architecture, where multiple FPGAs can be stacked on top of each other rather than being spread across a 2D plane. This provides lower off-chip latency with a smaller footprint. 3D MFS performance can be further improved through reduction in the number of interconnects by employing a serial communication. Nevertheless, electrical interconnects are limited in their performance due to latency. Moving a serial link from electrical domain to optical domain decreases off-chip delays improving the system frequency. Additionally, the selection of MFS routing architecture also has a substantial effect on the system performance. In this paper, we propose a novel 3D MFSs with different routing architectures that employ serialised optical interface improving the system frequency significantly. An experimental architecture evaluation framework and associated CAD tools were developed. The proposed architectures were experimentally evaluated and provided average system frequency gain of 37% across six benchmark circuits.
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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".