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Record W2094079707 · doi:10.1049/ip-cdt:20020449

Current dynamics-based macro-model for power simulation in a complex VLIW DSP processor

2002· article· en· W2094079707 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

VenueIEE Proceedings - Computers and Digital Techniques · 2002
Typearticle
Languageen
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer scienceVery long instruction wordDigital signal processingVery-large-scale integrationMacroCompilerEmbedded systemPower (physics)SoftwareComputer hardwareComputer architecture

Abstract

fetched live from OpenAlex

A methodology and a macro-modelling approach are presented for analysing low-level current dynamics at the instruction and program level for a complex VLIW DSP processor core. An instruction-level macro-model, whose input parameters can be extracted from the DSP core's assembly level program, is introduced for power modelling. For the first time, dynamic power models of algorithms are introduced and verified with real power measurements of a DSP processor core in a VLSI chip. Results from both cryptographic and bubble sort applications show that dynamic power can be modelled with an average error in energy estimation ranging from 0.3% to 9.7%. The instruction-level macro-model of power also supports different clock frequencies and compressed algorithmic traces, important for security aware compilers. In general, the research is important for analysing and modelling the impact of software on power, the design of embedded cryptographic VLSI systems that are safe from power attacks, and for reliable design by detecting the peak current values generated by the software application.

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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.928
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 communication0.0010.001
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 head0.035
GPT teacher head0.287
Teacher spread0.252 · 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