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P* Admissible Thermal-Aware Matrix Floorplanner for 3D ICs

2023· article· en· W4386953463 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
Topic3D IC and TSV technologies
Canadian institutionsMcGill University
Fundersnot available
KeywordsFloorplanThree-dimensional integrated circuitComputer scienceKey (lock)Reduction (mathematics)Integrated circuitMatrix (chemical analysis)Power (physics)ThermalComputer engineeringParallel computingAlgorithmComputational scienceEmbedded systemMathematicsMaterials science

Abstract

fetched live from OpenAlex

Over the past decade, three-dimensional (3D) integrated circuits (ICs) have matured as a promising solution for high-performance computing systems. Achieving high power density (>1 W/mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ), however, poses a critical thermal management challenge, as heat is prone to be trapped within the layers of the 3D structure. Early-stage power-aware floorplanning is crucial to address thermal challenges, supporting the evaluation of multiple design alternatives. In this paper, a P* admissible thermally aware floorplanning algorithm for 3D ICs is proposed. The operation principle of the algorithm is based on storing and manipulating the data of the functional blocks in a matrix form, supporting polynomial optimization and packing time. Simulation results on standard benchmarks (MCNC and GSRC) exhibit a significant improvement in key performance metrics. A reduction in area, temperature, and runtime of up to, respectively, 10.1%, 17.7%, and 89.6% are observed, as compared to the state-of-the-art.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.625
Threshold uncertainty score0.762

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.018
GPT teacher head0.257
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations3
Published2023
Admission routes1
Has abstractyes

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