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Record W2128701724 · doi:10.5555/2016802.2016811

FPGA glitch power analysis and reduction

2011· article· en· W2128701724 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

VenueInternational Symposium on Low Power Electronics and Design · 2011
Typearticle
Languageen
FieldEngineering
TopicVLSI and FPGA Design Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsGlitchField-programmable gate arrayPower analysisDynamic demandOverhead (engineering)Reduction (mathematics)Computer sciencePower (physics)Routing (electronic design automation)Embedded systemAlgorithmMathematicsCryptographyTelecommunicationsPhysics

Abstract

fetched live from OpenAlex

This paper presents a don't-care-based synthesis technique for reducing glitch power in FPGAs. First, an analysis of glitch power and don't-cares in a commercial FPGA is given, showing that glitch power comprises an average of 26.0% of total dynamic power. An algorithm for glitch reduction is then presented, which takes advantage of don't-cares in the circuit by setting their values based on the circuit's simulated glitch behavior. Glitch power is reduced by up to 49.0%, with an average of 13.7%, while total dynamic power is reduced by up to 12.5%, with an average of 4.0%. The algorithm is applied after placement and routing, and has zero area and performance overhead.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.713
Threshold uncertainty score0.702

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 head0.009
GPT teacher head0.210
Teacher spread0.201 · 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