MétaCan
Menu
Back to cohort
Record W3156265506 · doi:10.1504/ijcaet.2021.10037082

Latency-optimised 3D multi-FPGA system with serial optical interface

2021· article· en· W3156265506 on OpenAlexaff
Asmeen Kashif, Mohammad A.S. Khalid

Bibliographic record

VenueInternational Journal of Computer Aided Engineering and Technology · 2021
Typearticle
Languageen
FieldEngineering
Topic3D IC and TSV technologies
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsField-programmable gate arrayComputer scienceEmbedded systemInterface (matter)Latency (audio)Computer hardwareOperating systemTelecommunications

Abstract

fetched live from OpenAlex

Multi-FPGA systems (MFSs) are capable of prototyping large SoCs. However, planar 2D MFSs with electrical interconnections have broader spatial distribution and large off-chip delays. One good solution for this problem is to use a three-dimensional (3D) architecture, where multiple FPGAs can be stacked on top of each other rather than being spread across a 2D plane. This provides lower off-chip latency with a smaller footprint. 3D MFS performance can be further improved through reduction in the number of interconnects by employing a serial communication. Nevertheless, electrical interconnects are limited in their performance due to latency. Moving a serial link from electrical domain to optical domain decreases off-chip delays improving the system frequency. Additionally, the selection of MFS routing architecture also has a substantial effect on the system performance. In this paper, we propose a novel 3D MFSs with different routing architectures that employ serialised optical interface improving the system frequency significantly. An experimental architecture evaluation framework and associated CAD tools were developed. The proposed architectures were experimentally evaluated and provided average system frequency gain of 37% across six benchmark circuits.

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 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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.546
Threshold uncertainty score0.579

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)0.0000.000

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 head0.005
GPT teacher head0.196
Teacher spread0.190 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Computer Aided Engineering and TechnologySame topic3D IC and TSV technologiesFrench-language works237,207