Delay-line temperature sensors and VLSI thermal management demonstrated on a 60nm FPGA
Bibliographic record
Abstract
This paper presents a thermal sensing and VLSI thermal management scheme using an array of on-chip all-digital delay-line based temperature sensors. A fully digital self-calibration method that removes the temperature sensors' sensitivities to supply voltage and process variations is proposed. The proposed calibration method assigns a unique correction factor, NCto each sensor, making all the sensors' calibrated outputs to be the same at start-up. The correction factor is updated when supply voltage variations are detected. Only one calibration block is required to calibrate multiple delay-line based temperature sensors sequentially. For each additional sensor, only additional registers for storing NCare required. The proposed self-calibrated temperature sensors are demonstrated on an Altera Cyclone IV FPGA based VLSI thermal management system. Runtime thermal profiles for four cores mapped on the Cyclone IV FPGA chip using a hybrid dynamic thermal management (DTM) method are obtained. The percentage of time that each core spent in a particular temperature range is plotted in a histogram. A comparison of different DTM techniques demonstrates that the proposed hybrid DTM reduces the amount of time that the MPSoC spent at higher temperatures and larger thermal gradients, by 10% and 21%, respectively. In addition, the proposed hybrid DTM offers a 10% improvement in the average processing rate (instructions per second) when compared with the conventional global DFS approach.
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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 | 0.001 | 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".