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Record W2037455559 · doi:10.1109/tcad.2011.2165715

Accelerating FPGA Routing Through Parallelization and Engineering Enhancements Special Section on PAR-CAD 2010

2011· article· en· W2037455559 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

VenueIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · 2011
Typearticle
Languageen
FieldEngineering
TopicVLSI and FPGA Design Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSpeedupComputer scienceParallel computingField-programmable gate arrayRouterRouting (electronic design automation)Overhead (engineering)HeuristicGate arrayHeuristicsEmbedded systemComputer network

Abstract

fetched live from OpenAlex

We present parallelization and heuristic techniques to reduce the run-time of field-programmable gate array (FPGA) negotiated congestion routing. Two heuristic optimizations provide over 3× speedup versus a sequential baseline. In our parallel approach, sets of design signals are assigned to different processor cores and routed concurrently. Communication between cores is through the message passing interface communications protocol. We propose a geographic partitioning of signals into independent sets to help minimize the communication overhead. Our parallel implementation provides approximately 2.3× speedup using four cores and produces deterministic/repeatable results. When combined, the parallel and heuristic techniques provide over 7× speedup with four cores versus the router in the widely used Versatile Place and Route (VPR) FPGA placement/routing framework, with no significant impact on circuit speed or wirelength.

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 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 score0.956
Threshold uncertainty score1.000

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 head0.055
GPT teacher head0.220
Teacher spread0.164 · 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