Adaptive multi-domain thermal modeling and analysis for integrated circuit synthesis and design
Bibliographic record
Abstract
Chip-package thermal analysis is necessary for the design and synthesis of reliable, high-performance, low-power, compact integrated circuits (ICs). Many methods of IC thermal analysis suffer performance or accuracy problems that prevent use in IC synthesis and hinder use in architectural design. This article describes ISAC, a novel, fast, accurate thermal analysis system for use in IC synthesis and design. We present new, cooperative, temporal and spatial adaptation methods to dramatically accelerate accurate analysis. The proposed system unifies steady-state, time-domain, and frequency-domain analysis techniques. It is composed of our spatially-adaptive multigrid iterative solver, a new temporally and spatially adaptive asynchronous time marching solver, and a new spatially-adaptive frequency-domain moment matching solver. Together, these cooperative adaptation and multi-domain analysis techniques allow the proposed system to efficiently solve the static, short time scale, and long time scale variants of the IC thermal analysis problem. Experimental results demonstrate significant performance improvement over existing thermal analysis solutions. Our spatial adaptation techniques bring a 21.6x-690.0x speedup over recently-published steady-state thermal analysis techniques. Our unified spatial and temporal adaptation techniques, within our asynchronous time marching method, bring a 1,071x-1,890x speedup over other widely-used, time-domain thermal analysis techniques with less than 0.5% error. Our spatial adaptation techniques enable the efficient use of our frequency-domain thermal analysis technique, which brings a 10x-100x speedup over the fastest-known time-domain technique, when used for long time scale thermal analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".