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Record W2110209678 · doi:10.1109/iwsoc.2005.90

Performance improvement of configurable processor architectures using a variable clock period

2005· article· en· W2110209678 on OpenAlex
Bill Pontikakis, Yvon Savaria, François-Raymond Boyer

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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsComputer scienceSpeedupClock rateDigital clock managerField-programmable gate arrayVariable (mathematics)Task (project management)Quantization (signal processing)AccelerationCPU multiplierParallel computingEmbedded systemComputer hardwareClock skewClock signalJitterAlgorithmChipEngineeringTelecommunications

Abstract

fetched live from OpenAlex

Programmable and configurable processors are becoming increasingly popular for embedded wearable devices. In configurable processors technology it is a common practice to define specialized instructions in order to boost the performance of the device. These instructions may not fit in a single clock period and therefore, may require two clock periods for completion of a given task. In the past, we have proposed a method to generate a clock where each cycle can have a different length, and in this paper we investigate the performance gain it can give compared to standard clocking. Using our variable fractional clock period method, a gain of more than 10% in performance is easily obtained, with a maximum of 21%, compared to current best clocking techniques used in extensible configurable processors. We also show that the overall speedup of our method follows the well known Amdahl's law, but without quantization of the acceleration factor.

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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.432
Threshold uncertainty score0.364

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 science0.0010.000
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 head0.013
GPT teacher head0.245
Teacher spread0.232 · 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