Tensor Core GPU and Krylov Subspace-Based Algorithms for Multiport Large-Scale Circuit Reduction
Bibliographic record
Abstract
Due to the recent surge in the user demand for higher density of devices, higher operating frequencies and multi-function designs, signal propagation issues such as delay, attenuation, crosstalk and skin effects of interconnects become predominant.Also, the desire for low-power designs with lower operating voltages, sharp signal edges and heavy switching currents are making the design of power delivery networks extremely challenging.Efforts to accurately model and capture these effects pose the challenge of simulating circuits with large sizes and mixed frequency/time formulations. Model-order reduction techniques based onKrylov-subspace have been suggested in the literature to speed up the simulation of large circuits along with mixed frequency/time formulations.Arnoldi algorithm based on Modified Gram-Schmidt or Cholesky formulations have been used to construct the Krylov-subspace with orthogonal projections.These were advanced to preserve the passivity of the reduced-order model using the PRIMA algorithm.In the recent years, exploiting the emerging multi-core and GPU platforms, parallel Arnoldi and GPU based approaches were developed.In this thesis, Krylov subspace-based PRIMA algorithm will be further advanced 5 Development of the Proposed Tensor Core GPU-Based PRIMA (TC-PRIMA) 5.1 Proposed TC-TSQR Factorization with GPU . . . . . . . . . .5.2 Proposed Tensor Core Based QR Implementation in GPU . .vi 6 Numerical Results 6.1 Establishing Benchmark for Accuracy Comparison for the Proposed Method . . . . . . . . . . . . .
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".