MétaCan
Menu
Back to cohort
Record W1983241261 · doi:10.1109/fpt.2010.5681758

Deterministic multi-core parallel routing for FPGAs

2010· article· en· W1983241261 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
FieldComputer Science
TopicInterconnection Networks and Systems
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceRouting (electronic design automation)Field-programmable gate arrayRouterParallel computingQueueMulti-core processorStatic routingEmbedded systemRouting protocolComputer networkDistributed computing

Abstract

fetched live from OpenAlex

We consider coarse and fine-grained techniques for parallel FPGA routing on modern multi-core processors. In the coarse-grained approach, sets of design signals are assigned to different processor cores and routed concurrently. Communication between cores is through the MPI (message passing interface) communications protocol. In the fine-grained approach, the task of routing an individual load pin on a signal is parallelized using threads. Specifically, as FPGA routing resources are traversed during maze expansion, delay calculation, costing and priority queue insertion for these resources execute concurrently. The proposed techniques provide deterministic/repeatable results. Moreover, the coarse and fine-grained approaches are not mutually exclusive and can be used in tandem. Results show that on a 4-core processor, the techniques improve router run-time by ~2.1×, on average, with no significant impact on circuit speed performance or interconnect resource usage.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.985
Threshold uncertainty score0.266

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 head0.051
GPT teacher head0.298
Teacher spread0.247 · 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

Citations43
Published2010
Admission routes1
Has abstractyes

Explore more

Same topicInterconnection Networks and SystemsFrench-language works237,207