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Record W2112963544 · doi:10.1109/82.974777

Difference metric soft-out-put detection: architecture and implementation

2001· article· en· W2112963544 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Circuits and Systems II Analog and Digital Signal Processing · 2001
Typearticle
Languageen
FieldComputer Science
TopicVLSI and Analog Circuit Testing
Canadian institutionsUniversity of Toronto
FundersCMC Microsystems
KeywordsApplication-specific integrated circuitVery-large-scale integrationMetric (unit)CMOSComputer scienceDecodesSoft errorKernel (algebra)InterconnectionElectronic engineeringAlgorithmLimiterDecoding methodsParallel computingMathematicsEmbedded systemEngineeringDiscrete mathematicsTelecommunications

Abstract

fetched live from OpenAlex

The forward-backward (FB, also known as the MAP or BCJR) detection algorithm provides "soft" reliability estimates for each bit that it decodes. This paper presents a VLSI architecture for soft-output forward-backward detection of Class-IV partial response signaling (PR4) used in magnetic recording. A difference metric version of the FB algorithm is derived. A novel low-complexity architecture implements the computational kernel as a limiter. A 0.35-/spl mu/m 3-level metal CMOS ASIC was implemented and verified to operate at 20 MHz (20 Mbps), the highest speed of our IC tester. Simulations predict operation of up to 150 Mbps.

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 categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.992
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
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
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 head0.025
GPT teacher head0.250
Teacher spread0.225 · 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