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Record W2104724816 · doi:10.1109/iscas.2005.1465615

Minimal Activity Mixed-Signal VLSI Architecture for Real-Time Linear Transforms in Video

2005· article· en· W2104724816 on OpenAlexaff
Rafal Karakiewicz, Roman Genov

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAnalog and Mixed-Signal Circuit Design
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDramComputer scienceCMOSUnary operation16-bitVery-large-scale integrationMultiplication (music)Binary numberComputer hardwareParallel computingElectronic engineeringMathematicsArithmeticEngineeringEmbedded system

Abstract

fetched live from OpenAlex

The mixed-signal processor performs digital vector-matrix multiplication using internally analog fine-grain parallel computing. The three-transistor CID/DRAM unit cell combines single-bit dynamic storage, binary multiplication, and zero-latency analog accumulation. Matrix coefficients are stored in a bit-parallel form. Delta-sigma analog-to-digital conversion of the analog array outputs is combined with oversampled unary coding of the digital inputs. Sorting of unary inputs results in at most a single input line transition for arbitrary multi-bit inputs. This amounts to a linear gain in energy efficiency of the computational array in the number of bits of the input vector. The 256 /spl times/ 128 CID/DRAM processor with integrated 128 delta-sigma ADC measures 3 mm /spl times/ 3 mm in 0.5 /spl mu/m CMOS and delivers 6.5 GMACS dissipating 5.9 mW of power. CID/DRAM array dynamic power dissipation is reduced by a factor of four through sorting 8-bit inputs.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.010
GPT teacher head0.219
Teacher spread0.209 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations0
Published2005
Admission routes1
Has abstractyes

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