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Parallel CRC On An FPGA At Terabit Speeds

2022· article· en· W4311839535 on OpenAlexaff
Qianfeng Shen, Juan Camilo Vega, Paul Chow

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Semiconductor Devices and Circuit Design
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceCyclic redundancy checkField-programmable gate arrayNetwork packetRedundancy (engineering)EthernetParallel computingTerabitReliability (semiconductor)Embedded systemComputer hardwareComputer networkOperating system

Abstract

fetched live from OpenAlex

The Cyclic Redundancy Check Algorithm (CRC) is critical for ensuring high data reliability in serial communication such as Ethernet networks, allowing for the detection of corrupted packets with a programmable and arbitrarily small probability of failure. The baseline algorithm, however, is highly serialized due to read after write (RAW) dependencies, preventing efficient parallelization of the algorithm for use in hardware. We built a fully parameterizable open-source IP core that has no such dependencies to produce the equivalent result as the baseline CRC algorithm but in a form that can be fully parallelized, with fully automated pipelining, which works for any CRC polynomial, and with a low-resource end-of-packet alignment. This allows for up to 64-bit CRC to be computed in an FPGA at 4 Tbps.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.908
Threshold uncertainty score0.990

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)0.0110.000

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 head0.025
GPT teacher head0.244
Teacher spread0.219 · 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.

Study designNot applicable
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

Citations5
Published2022
Admission routes1
Has abstractyes

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