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Record W2949207837 · doi:10.1109/mssc.2019.2910619

Ultra-Short-Reach Interconnects for Die-to-Die Links: Global Bandwidth Demands in Microcosm

2019· article· en· W2949207837 on OpenAlexaff
Behzad Dehlaghi, Nijwm Wary

Bibliographic record

VenueIEEE Solid-State Circuits Magazine · 2019
Typearticle
Languageen
FieldComputer Science
TopicInterconnection Networks and Systems
Canadian institutionsUniversity of TorontoUniversity of Calgary
Fundersnot available
KeywordsBandwidth (computing)Die (integrated circuit)Signal integrityPower consumptionElectrical impedanceChipInterconnectionElectronic engineeringElectrical engineeringComputer scienceEngineeringTelecommunicationsPower (physics)PhysicsMechanical engineering

Abstract

fetched live from OpenAlex

Bandwidth density metrics are emerging as essential criteria for high performance serial links in addition to energy efficiency. These include bandwidth per bump, per wire, and per millimeter of cross-sectional bus width as well as per-unit chip area. In comparison to conventional board level chip-to-chip interconnects over a PCB, USR interfaces between copackaged dies increase density with relatively modest frequency-dependent losses and, thus, low power consumption. To achieve the highest possible bandwidth density over USR links, traditional assumptions about high performance electrical interconnects such as termination impedances must be reexamined. For example, single ended signaling can offer comparable signal integrity and higher bandwidth density than differential signaling, particularly if there is a pitch constraint on the die-to-die traces. Moreover, termination of the USR links has a significant impact on their performance, so a detailed analysis of the terminating impedances is required to optimize link performance.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0030.004
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.019
GPT teacher head0.279
Teacher spread0.260 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations19
Published2019
Admission routes1
Has abstractyes

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Same venueIEEE Solid-State Circuits MagazineSame topicInterconnection Networks and SystemsFrench-language works237,207