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Record W2953004880 · doi:10.1109/fccm.2019.00018

RapidRoute: Fast Assembly of Communication Structures for FPGA Overlays

2019· article· en· W2953004880 on OpenAlexaff
Leo Liu, Jay Weng, Nachiket Kapre

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInterconnection Networks and Systems
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer scienceRouterHeuristicsField-programmable gate arrayOverlayPolygon meshRouting (electronic design automation)ReuseEmbedded systemOverhead (engineering)Parallel computingComputer architectureComputer networkOperating systemEngineering

Abstract

fetched live from OpenAlex

We can implement relocatable, bus-based communication structures on Xilinx FPGAs using RapidWright while delivering competitive frequency, single digit speedups in execution time, and orders of magnitude reduction in memory usage over Xilinx Vivado 2017.2. We develop RapidRoute, a custom router that exploits symmetry in placement and routing of bus endpoints, caching of reusable route segments, selective multi-threading of the router engine, and abutment-friendly tiling heuristics. The key idea is to reduce the amount of work necessary to generate these communication structures through the use of search heuristics, parallelism, and reuse. We are able to outperform Vivado router by as much as 8× for topologies ranging from 1D rings, torii, and meshes, while taking 1000× lower memory footprint, and delivering timing with 0.2ns of Vivado. RapidRoute opens the door to building a family of custom routing tools for constructing FPGA overlays for various application domains.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0170.002

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 head0.015
GPT teacher head0.251
Teacher spread0.236 · 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 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

Citations9
Published2019
Admission routes1
Has abstractyes

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