Fast Prediction of Temperature Evolution in Electronic Devices for Run-Time Thermal Management Applications
Bibliographic record
Abstract
Increase of non-uniform power density and high switching frequency has presented new challenges in predicting transient temperature response to fast-changing power inputs in advanced electronic devices. While the computational effort with direct calculation through the finite element model (FEM) is expensive, various methods of model reduction with drastically improved computing speed have been developed for calculation of dynamic thermal responses of the electronic systems. However, those methods’ still-considerable computational time consumption inhibits their practices in real-time temperature prediction and dynamic thermal management (DTM) applications. This work presents a fast algorithm for predicting temperature evolution in electronic devices subjected to multiple heat source excitations. It utilizes the equivalent thermal RC network for model reductions, and adopts recursive infinite impulse response (IIR) digital filters for accelerated computation in discrete time-domain. The algorithm is validated by comparison to existing convolution integral methods, yielding excellent agreement with several orders of magnitude improvement in computation efficiency. Due to its simplicity in implementation, the algorithm is very suitable for run-time evaluation of temperature response for dynamic power management applications.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".