Delay Monitor Circuit and Delay Change Measurement Due to SEU in SRAM-Based FPGA
Bibliographic record
Abstract
This paper presents a monitor circuit designed for the detection of extra combinational delays in a high-frequency SRAM-based field-programmable gate array (FPGA). Since in most of the SRAM-based FPGAs, more than 90% of the configuration bits control the routing resources, systems designed on FPGA are particularly vulnerable to interconnection delay changes (DCs) caused by single-event upset (SEU) affecting the configuration memory. The proposed monitor is part of a mitigation technique dedicated to protect the circuit routing delay integrity while the system is being exposed to SEUs generated by radiation. Experimental results show that the probability of DC occurrence can increase when the number of DCs affecting a node increases. Indeed, this increase depends on the configurable interconnection network and design placement in FPGA. Delay measurements using the proposed monitor revealed the existence of single DCs ranging from 29 to 151 ps. Also, cumulative DCs in the range of 279-309 ps being the results of an extra interconnection network added by SEUs have been detected. Measured delay values are in good agreement with those observed experimentally under proton radiation and also circuit-level simulations and emulations.
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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) | 0.000 | 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 | 0.001 | 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".