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Record W2161307877 · doi:10.1109/pacrim.2007.4313248

High Level Fixed Point VLSI Design with Automated Clock Gating

2007· article· en· W2161307877 on OpenAlexaff
Nainesh Agarwal, N.J. Dimopoulos

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicLow-power high-performance VLSI design
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsClock gatingComputer scienceEmbedded systemVery-large-scale integrationPower gatingBenchmark (surveying)Computer architectureComputer hardwareGatingEngineeringClock signalClock skewTransistorElectrical engineering

Abstract

fetched live from OpenAlex

Here we present a high level VLSI design platform, which supports the use of fixed point operations and automated clock gating of registers. This platform has been implemented by extending the CoDeL design suite. CoDeL allows hardware description at the algorithm level, and thus dramatically reduces design time. Also, it automatically inserts clock gating at the behavioral level to reduce dynamic power dissipation in the resulting architecture. This is, to our knowledge, the first hardware design environment that allows an algorithmic description of a component and yet produces a power aware design. We use the DSPstone benchmark to thoroughly evaluate this fixed point design platform for the design of power efficient DSP architectures. We find that, compared to a modern DSP, the CoDeL platform produces designs with somewhat slower run times but dramatically lower power dissipation. Next we use power analysis to compare the effectiveness of CoDeL's automated clock gating as compared to automated clock gating using synopsys tools. A simulation based power analysis shows that CoDeL's clock gating provides 16% more power savings than Synopsys' automated clock gating alone.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.518
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.019
GPT teacher head0.210
Teacher spread0.191 · 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.

Study designSimulation or modeling
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

Citations1
Published2007
Admission routes1
Has abstractyes

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