Fast Voltage Transients on FPGAs: Impact and Mitigation Strategies
Bibliographic record
Abstract
As FPGAs grow in size and speed, so too does their power consumption. Power consumption on recent FPGAs has increased to the point that it is comparable to that of high-end CPUs. To mitigate this problem, power reduction techniques such as dynamic voltage scaling (DVS) and clock gating can potentially be applied to FPGAs. However, it is unclear whether they are safe in the presence of fast voltage transients. These fast voltage transients are caused by large changes in activity which we believe are common in most designs. Previous work has shown that it is these fast voltage transients that produce the largest variations in delay. In our work, we measure the impact transients have on applications and present a mitigation strategy to prevent them from causing timing failures. We create transient generators that are able to significantly reduce an application's measured Fmax, by up to 25. We also show that transients are very fast and produce immediate timing impact and hence transient mitigation must occur within the same clock cycle as the transient. We create a clock edge suppressor that is able to detect when a transient event is happening and delay the clock edge, thus preventing any timing failures. Using our clock edge suppressor, we show that we can run an application at full frequency in the presence of fast voltage transients, thereby enabling more aggressive DVS approaches and larger power savings.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".