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Record W2112239176 · doi:10.1109/icnsc.2015.7116095

Energy efficient dual-issue processor for embedded applications

2015· article· en· W2112239176 on OpenAlexaff
Hanni Lozano, M.R. Ito

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer scienceEmbedded systemVery long instruction wordEfficient energy useInstruction schedulingEnergy consumptionInstructions per cycleReduced instruction set computingScheduling (production processes)Field-programmable gate arrayInstruction setSuperscalarComputer architectureParallel computingComputer hardwareCentral processing unitOperating systemDynamic priority schedulingScheduleElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

Low cost and low power scalar embedded processors can issue only a single instruction per cycle which results in longer execution time for applications and consequently lower energy efficiency. Superscalar processor that can issue multiple instructions per cycle are more energy efficient than scalar processors, however, they consume more power which is severely limited in embedded systems that operate on small batteries or energy harvesting power sources. In this paper we propose an energy efficient dual-issue embedded processor that can deliver up to 60% improvement in IPC (instruction-per-cycle) performance with less than 20% increase in power consumption compared to a single-issue embedded processor. In contrast to traditional multi-issue embedded processors that use power intensive superscalar techniques to extract instruction-level parallelism from applications, the proposed processor uses simple hardware techniques to resolve instruction scheduling conflicts. The processor is optimized for implementation on low cost FPGAs suitable for power sensitive embedded applications.

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 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: Methods
Teacher disagreement score0.796
Threshold uncertainty score0.277

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 head0.024
GPT teacher head0.288
Teacher spread0.263 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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

Citations2
Published2015
Admission routes1
Has abstractyes

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