Thermal-Aware Design and Flow for FPGA Performance Improvement
Bibliographic record
Abstract
To ensure reliable operation of circuits under elevated temperatures, designers are obliged to put a pessimistic timing margin proportional to the worst-case temperature (Tworst), which incurs significant performance overhead. The problem is exacerbated in deep-CMOS technologies with increased leakage power, particularly in Field-Programmable Gate Arrays (FPGAs) that comprise an abundance of leaky resources. We propose a two-fold approach to tackle the problem in FPGAs. For this end, we first obtain the performance and power characteristics of FPGA resources in a temperature range. Having the temperature-performance correlation of resources together with the estimated thermal distribution of applications makes it feasible to apply minimal, yet sufficient, timing margin. Second, we show how optimizing an FPGA device for a specific thermal corner affects its performance in the operating temperature range. This emphasizes the need for optimizing the device according to the target (range of) temperature. Building upon this observation, we propose thermal-aware optimization of FPGA architecture for foreknown field conditions. We performed a comprehensive set of experiments to implement and examine the proposed techniques. The experimental results reveal that thermal-aware timing on FPGAs yields up to 36.5% performance improvement. Optimizing the architecture further boosts the performance by 6.7%.
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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 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".