Reliability- and process variation-aware placement for FPGAs
Bibliographic record
Abstract
Abstract—Negative bias temperature instability (NBTI) signif-icantly affects nanoscale integrated circuit performance and re-liability. The degradation in threshold voltage (Vth) due to NBTI is further affected by the initial value of Vth from fabrication-induced process variation (PV). Addressing these challenges in embedded FPGA designs is possible, as FPGA reconfigurablility can be exploited to measure the exact timing degradation of an FPGA due to the joint effect of NBTI and PV at run time with low overhead. The gathered information can then be used to improve the run-time performance and reliability of FPGA designs without targeting the pessimistic worst case. In this paper, we present joint NBTI/PV-aware placement techniques for FPGAs, including NBTI/PV-aware timing anal-ysis, region-based delay estimation, and a new move-acceptance procedure. To evaluate the proposed techniques, we combine PV measurements from 15 Xilinx Virtex-II Pro FPGAs with a model of NBTI. The proposed techniques reduce the effect of NBTI/PV by more than 60 % for over 60 % of the 15 FPGA chips used in the experiments, with a typical run-time overhead of 1.4–1.8X. The standalone move-acceptance procedure also produces good results with negligible run-time overhead, making it suitable for online FPGA compilation and optimization flows. I.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".