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Record W2118988958 · doi:10.1145/1344671.1344680

WireMap

2008· article· en· W2118988958 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
FieldEngineering
TopicVLSI and FPGA Design Techniques
Canadian institutionsXilinx (Canada)
Fundersnot available
KeywordsLookup tableReduction (mathematics)Computer scienceHeuristicEnhanced Data Rates for GSM EvolutionRouting (electronic design automation)Path (computing)AlgorithmMathematicsEmbedded systemTelecommunicationsComputer networkArtificial intelligence

Abstract

fetched live from OpenAlex

This paper presents a new technology mapper, WireMap. The mapper uses an edge flow heuristic to improve the routability of a mapped design. The heuristic is applied during the iterative mapping optimization to reduce the total number of pin-to-pin connections (or edges). The average edge reduction of 9.3% is achieved while maintaining depth and LUT count of state-of-the-art technology mapping. Placing and routing the resulting netlists leads to an 8.5% reduction in the total wire length, a 6.0% reduction in minimum channel width, and a 2.3% reduction in critical path delay. Applying WireMap has an additional advantage of reducing an average number of inputs of LUTs without increasing the total LUT count and depth. The percentages of 5- and 6-LUTs in a typical design are reduced, while the percentages of 2-, 3-, and 4-LUTs are increased. These smaller LUTs can be merged into pairs and implemented using the dual output LUT structure found in commercial FPGAs. WireMap leads to 9.4% fewer dual-output LUTs after merging

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.910
Threshold uncertainty score0.182

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 head0.013
GPT teacher head0.165
Teacher spread0.152 · 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

Citations30
Published2008
Admission routes1
Has abstractyes

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