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Record W4231119401 · doi:10.1145/639932.639934

Switching activity analysis and pre-layout activity prediction for FPGAs

2003· article· en· W4231119401 on OpenAlexaff
Jason H. Anderson, Farid N. Najm

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicLow-power high-performance VLSI design
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDelay calculationComputer scienceElmore delayField-programmable gate arrayPropagation delayRouting (electronic design automation)Logic gateSequential logicLogic synthesisProcessing delayCMOSElectronic engineeringTransmission delayAlgorithmEmbedded systemComputer networkEngineeringNetwork packet

Abstract

fetched live from OpenAlex

It is well-known that dynamic power dissipation in digital CMOS circuits depends linearly on switching activity. In this paper, we study switching activity in a commercial FPGA and propose a novel approach to pre-layout activity prediction. We examine how switching activity on a net changes when delays are zero (zero delay activity) versus when logic delays are considered (logic delay activity) versus when both logic and routing delays are considered (routed delay activity). Low-power synthesis and early power estimation are typically done on the basis of zero delay activity values, with the assumption that such values correlate well with routed delay activity values. We investigate whether this assumption is valid for FPGA technologies, where critical path delay is often dominated by interconnect delay. We then present an approach for early prediction of routed delay activity values. Our approach is novel in that it estimates each net's routed delay activity using only zero or logic delay activity values along with structural and functional properties of a circuit. Results show that in comparison with zero or logic delay activity values, the predicted activity values are substantially more representative of routed delay activity values.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.433
Threshold uncertainty score0.704

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 communication0.0000.001
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 head0.008
GPT teacher head0.212
Teacher spread0.204 · 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
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
Published2003
Admission routes1
Has abstractyes

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