In-Situ Timing Diagnosis of PDN and Configuration-Upset-Induced Routing Delay Degradation in SRAM-based FPGAs
Bibliographic record
Abstract
Timing degradation in SRAM-based FPGAs arises from multiple physical mechanisms that manifest differently in the routing fabric, most notably power-distribution-network (PDN) marginality and configuration-induced routing perturbations. While existing in-situ timing monitors can detect delay shifts, they typically provide limited insight into the physical origin, spatial structure, or statistical characteristics of the degradation. This paper presents a scalable in-situ timing diagnosis architecture that enables fine-grained, routing-aware characterization of timing behavior directly within the FPGA fabric during normal operation. The proposed approach combines non-intrusive delay taps placed at routing switch-matrix boundaries with distributed phase-swept delay monitoring elements and centralized statistical analysis. By extracting probabilistic delay distributions rather than binary timing margins, the framework captures both mean delay shifts and timing variability across spatially distributed routing locations. Experimental results obtained on a modern SRAM-based FPGA show that PDN-induced timing degradation produces globally correlated delay shifts with minimal change in variance, whereas routing-induced perturbations exhibit localized, topology-dependent delay growth and increased timing dispersion. Spatial correlation analysis and two-dimensional correlation heatmaps further reveal distinct signatures that enable systematic differentiation between these mechanisms. The presented architecture operates concurrently with an active user design and does not require external instrumentation, radiation sources, or design modification. These results establish a practical foundation for in-situ timing diagnosis, reliability assessment, and architecture-aware timing management in large FPGA-based systems.
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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) | 0.000 | 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 | 0.000 | 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".