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Record W3003276132 · doi:10.1109/tcsi.2020.2969325

Polar Compiler: Auto-Generator of Hardware Architectures for Polar Encoders

2020· article· en· W3003276132 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 Circuits and Systems I Regular Papers · 2020
Typearticle
Languageen
FieldComputer Science
TopicError Correcting Code Techniques
Canadian institutionsMcGill University
FundersFundamental Research Funds for the Central UniversitiesSoutheast UniversityNatural Science Foundation of Jiangsu ProvinceNational Natural Science Foundation of China
KeywordsCompilerComputer scienceEncoderParallel computingComputer hardwarePolarComputer architectureProgramming languageOperating system

Abstract

fetched live from OpenAlex

Polar codes have been standardized for enhanced mobile broadband (eMBB) control channels and been considered by other applications. Though there are lots of works on polar encoder implementations, the manual design is laborious regarding various application requirements. This paper devotes itself in proposing a compiler to automatically generate target polar encoders in Verilog HDL files, given code length, parallelism level, and stage number. This compiler is based on uniform formula representations of pipelined or stage-folded polar encoders. Thanks to the compiler, designers have been freed from manual design and enabled to conduct hardware optimization in design space with constraints on area, latency, power, or throughput. Implementation results show that polar encoders generated by the compiler are more efficient than the state-of-the-art ones in terms of area and energy.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.978
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 science0.0010.000
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 head0.027
GPT teacher head0.238
Teacher spread0.211 · 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