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Record W3150716070 · doi:10.1109/dac.2006.229229

A family of cells to reduce the soft-error-rate in ternary-CAM

2006· article· en· W3150716070 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

VenueProceedings - ACM IEEE Design Automation Conference · 2006
Typearticle
Languageen
FieldEngineering
TopicRadiation Effects in Electronics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSoft errorComputer scienceReduction (mathematics)Error detection and correctionWord error rateContent-addressable memoryTernary operationWord (group theory)Computer hardwareEmbedded systemArithmeticElectronic engineeringAlgorithmEngineeringSpeech recognitionArtificial intelligenceMathematicsProgramming language

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 ternary content-addressable memory (CAM) design with high immunity to SEU. 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. We propose a family of TCAM cells that reduce the SER at the cost of some area increase. An SER reduction of up to 40% can be obtained with a 18% increase of area; another design reduces the SER by 16% with only a 5% 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)0.001
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: Empirical
Teacher disagreement score0.344
Threshold uncertainty score0.824

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.026
GPT teacher head0.245
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