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Record W2159493595 · doi:10.1109/isqed.2004.1283711

Stacked FSMD: a power efficient micro-architecture for high level synthesis

2004· article· en· W2159493595 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDatapathComputer scienceOverhead (engineering)ArchitectureAbstractionSimple (philosophy)High-level synthesisComputer architectureController (irrigation)Metric (unit)Finite-state machineEmbedded systemState (computer science)Range (aeronautics)Computer engineeringDistributed computingProgramming languageEngineeringField-programmable gate array

Abstract

fetched live from OpenAlex

In this paper, we argue that the classic micro-architecture model, namely finite state machine with datapath (FSMD), cannot handle procedure abstraction needed by complex applications. This presents one of the major obstacles for the adoption of high-level synthesis technology in practice. We propose a simple extension of FSMD, called stacked FSMD, which mimics the procedure linkage concepts in software. We demonstrate that the new micro-architecture can not only fully support procedure calls, but also be made power efficient by a technique called region-based partitioning, which can be applied directly at the behavioral level with the assistance of simple metric evaluated at the behavioral level. With a rigorous experimental procedure, we show that the controller power saving achieved can range from 12 % to 68 % with modest overhead in area.

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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.722
Threshold uncertainty score0.521

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 head0.018
GPT teacher head0.248
Teacher spread0.230 · 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