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Record W4312576493 · doi:10.1109/tvlsi.2022.3214793

A Robust Integrated Power Delivery Methodology for 3-D ICs

2022· article· en· W4312576493 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Very Large Scale Integration (VLSI) Systems · 2022
Typearticle
Languageen
FieldEngineering
Topic3D IC and TSV technologies
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaFonds de recherche du Québec
KeywordsNotationIntegrated circuitComputer sciencePower (physics)Dimension (graph theory)Power domainsElectronic engineeringTopology (electrical circuits)VoltageMathematicsElectrical engineeringEngineeringPhysicsArithmetic

Abstract

fetched live from OpenAlex

The inherent advantages of three-dimensional (3-D) integrated circuits (ICs) are well-aligned with the continuous demand for increased density of functionality, reduced latency, the power dissipation of communication, and heterogeneity of modern applications. Delivering power efficiently to highly heterogeneous voltage domains across the tiers of a 3-D IC is, however, a significant challenge. To address the power delivery challenge in 3-D ICs, a robust integrated power delivery methodology is proposed in this article. Recent advancements in the fabrication of high-density integrated passive components, and the area that is available in the vertical dimension of the 3-D construct, are exploited in this work to enable an efficient and robust power delivery system for 3-D ICs. In the proposed approach, one or more layers within the 3-D structure are dedicated to power conversion and regulation, namely, power layers (PLs). A design exploration stage is also provided to determine the number of PLs, distribution of resources between power and functional layers (FLs), assignment of voltage domains to PLs, and voltage levels across the power delivery system. The proposed methodology is compared to three other power delivery topologies and exhibits 1.4– <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$38\times $ </tex-math></inline-formula> and 1.4– <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$7.1\times $ </tex-math></inline-formula> improvement in, respectively, voltage drop and power efficiency. Results are normalized to the total on- and off-chip area dedicated to power conversion and regulation in each topology.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.958
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.047
GPT teacher head0.245
Teacher spread0.198 · 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