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Odin-II Partial Technology Mapping for Yosys Coarse-grained Netlists in VTR

2022· article· en· W4282043149 on OpenAlexaff
Seyed Alireza Damghani, Kenneth B. Kent

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicVLSI and Analog Circuit Testing
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsVerilogComputer scienceRouting (electronic design automation)Computer architectureInterface (matter)Embedded systemParallel computingField-programmable gate array

Abstract

fetched live from OpenAlex

The Verilog-to-routing (VTR) front-end interface for Verilog compilation, Odin-II, lacks complete support for the Verilog-2005 standard. However, Odin-II provides complex partial mapping for balancing soft logic and hard blocks. Yosys, an open framework for RTL synthesis, provides extensive support for HDLs. However, the Yosys flow forces the user to decide the discrete circuit implementation manually. This research proposes improving device utilization and simplifying the flow by automating complex logic decisions with architecture awareness. According to VTR architectures, hard/soft logic trade-off decisions and heterogeneous logic inference have become available for such coarse-grained BLIF files. Yosys+Odin-II demonstrates promising results by lowering resource consumption, shrinking final circuit footprint, and reducing overall routed wire length, while other criteria remain approximately the same.

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 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: Empirical · Consensus signal: none
Teacher disagreement score0.898
Threshold uncertainty score0.442

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.029
GPT teacher head0.241
Teacher spread0.213 · 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 teacher head, 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

Citations1
Published2022
Admission routes1
Has abstractyes

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