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Record W2123761815 · doi:10.1109/iscas.2008.4542170

A dynamic address decode circuit for implementing range addressable look-up tables

2008· article· en· W2123761815 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicNetwork Packet Processing and Optimization
Canadian institutionsUniversity of Windsor
FundersCMC Microsystems
KeywordsComputer scienceLookup tableDecoding methodsScalabilityTable (database)Content-addressable memoryMatching (statistics)Electronic circuitComputer architectureComputer hardwareLogic gateComputer engineeringParallel computingEmbedded systemAlgorithmEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

A Range Addressable Look Up Table (RALUT) is a non-linear memory storage element that has been shown to significantly reduce hardware requirements for matching data in particular applications. However, its ability to perform parallel pattern matching on large words can be applied in many areas. Most of the RALUT circuits presented in literature thus far are built with logic gates and tri-state buffers so that they are easily synthesizable and implemented with other components of the overall design. These circuits are not competitive with modern memory in terms of area, timing, power and functionality. The only significant difference between a RALUT and a standard LUT is the address decoding system. In this paper, we will show a preliminary dynamic address decode circuit which can be used to build a scalable full custom read-only RALUT implementations. We will show significant reductions in area, timing and power compared to a previously published synthesized version.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.928
Threshold uncertainty score0.600

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 studies0.0010.000
Scholarly communication0.0000.001
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 head0.044
GPT teacher head0.279
Teacher spread0.235 · 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