Experimental evaluation and comparison of time-multiplexed multi-FPGA routing architectures
Bibliographic record
Abstract
Multi-FPGA systems (MFS) are indispensable for emulating multi-million gates integrated circuits (ICs) for the purpose of functional design verification before IC fabrication. However, with every new generation of FPGAs, the ratio between the logic capacity and the number of inputs and outputs is also increasing. Consequently, the limited FPGA input/output (I/O) pins impose a constraint when the number of inter-FPGA nets greatly exceeds the inter-FPGA physical tracks. This problem is addressed by serializing multiple cut nets using time multiplexing technique. Besides I/O resources, routing architecture also exercises a strong effect on the cost, speed and routability of MFS. In this paper, we compare the achieved system performance in two routing architectures: Completely Connected Graph (CCG) and Torus, when time multiplexing is employed. Six benchmark circuits have been partitioned such that per FPGA logic utilization is upto 60%. However, even with such reasonable logic consumption, the required I/O usage is observed to be 5-11 times more than the available I/O pins, thus employing time multiplexing. Experimental results show that CCG achieves higher performance as compared to Torus for the given range of TDM ratios. However, Torus can provide better cost/performance ratio for higher TDM ratios.
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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 | 0.001 | 0.001 |
| 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".