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Record W2102892811 · doi:10.1145/1462586.1462587

Static and Dynamic Memory Footprint Reduction for FPGA Routing Algorithms

2009· article· en· W2102892811 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

VenueACM Transactions on Reconfigurable Technology and Systems · 2009
Typearticle
Languageen
FieldEngineering
TopicVLSI and FPGA Design Techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer scienceRouting (electronic design automation)Static routingMemory footprintField-programmable gate arrayPolicy-based routingMultipath routingFootprintDistributed computingParallel computingEmbedded systemRouting protocol

Abstract

fetched live from OpenAlex

This article presents techniques to reduce the static and dynamic memory requirements of routing algorithms that target field-programmable gate arrays. During routing, memory is required to store both architectural data and temporary routing data. The architectural data is static, and provides a representation of the physical routing resources and programmable connections on the device. We show that by taking advantage of the regularity in FPGAs, we can reduce the amount of information that must be explicitly represented, leading to significant memory savings. The temporary routing data is dynamic, and contains scoring parameters and traceback information for each routing resource in the FPGA. By studying the lifespan of the temporary routing data objects, we develop several memory management schemes to reduce this component. To make our proposals concrete, we applied them to the routing algorithm in VPR and empirically quantified the impact on runtime memory footprint, and place and route time.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.962
Threshold uncertainty score0.758

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 head0.014
GPT teacher head0.237
Teacher spread0.224 · 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