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Record W2014466003 · doi:10.1109/cicc.2006.320887

A Soft-Error Tolerant Content-Addressable Memory (CAM) Using An Error-Correcting-Match Scheme

2006· article· en· W2014466003 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
TopicRadiation Effects in Electronics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSoft errorComputer scienceHamming distanceError detection and correctionHamming codeScheme (mathematics)Computer hardwareBinary numberContent-addressable memoryDissipationCoding (social sciences)Decoding methodsElectronic engineeringArithmeticAlgorithmEngineeringArtificial intelligenceBlock code

Abstract

fetched live from OpenAlex

Modern integrated circuits require careful attention to the soft-error rate (SER) resulting from bit upsets, which are normally caused by alpha particle or neutron hits. These events, also referred to as single-event upsets (SEUs), will become more problematic in future technologies. This paper presents a binary content-addressable memory (CAM) design with high immunity to SEUs. Conventionally, error-correcting codes (ECC) have been used in SRAMs to address this issue, but these techniques are not immediately applicable to CAMs because they depend on processing the full contents of the memory word outside the array, which is not possible in a normal CAM access. The proposed design consists of a new matching technique that uses coding to increase the Hamming distance between words, in conjunction with a modified matchline sensing scheme. The result is a CAM design that reduces the SER with no increase in delay or power dissipation, and with only a 12% increase in area

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
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 science0.0000.000
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 head0.039
GPT teacher head0.257
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

Quick stats

Citations72
Published2006
Admission routes1
Has abstractyes

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Same topicRadiation Effects in ElectronicsFrench-language works237,207