Frequency-Domain Power Delivery Network Self-Characterization in FPGAs for Improved System Reliability
Bibliographic record
Abstract
Modern field-programmable gate arrays (FPGAs) operate at a core voltage around 1 V and therefore even small voltage fluctuations lead to timing violations and logic errors. The power delivery network (PDN) between the voltage regulator and the FPGA core must be carefully designed to achieve a low output impedance over a broad range of frequencies. Simulation tools are commonly used to estimate the impedance, however, they do not account for aging, component variations, and inaccurate modeling of parasitic elements, all of which lead to PDN design deviation. In this paper, two schemes are presented: first, to extract the dc resistance in the power delivery path, and second, to identify the high impedance frequency band(s) in the PDN. The embedded impedance extraction tool is synthesized within the FPGA load, in coordination with a mixed-signal current-mode dc-dc converter. A new self-calibrated carry-chain-based analog-to-digital converter (CC-ADC) is used for high-speed sampling of the core voltage. The proposed schemes are demonstrated on an Intel Cyclone IV FPGA board. Real-time IR-drop compensation is shown to eliminate logic errors in an finite impulse response filter application. It is also shown that the fail/pass map of a crossbar application matches well with the extracted impedance profile versus voltage and frequency. By modifying the PDN based on the extracted results, the voltage operating range and reliability of the crossbar application are greatly extended.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".