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Record W2000502252 · doi:10.1109/ainaw.2007.196

Hardware-Software Cosynthesis of Multiprocessor Embedded Architectures

2007· article· en· W2000502252 on OpenAlexaff
Gul N. Khan, Usman Ahmed

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicEmbedded Systems Design Techniques
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsComputer scienceHypercubeMultiprocessingScheduling (production processes)Parallel computingEmbedded systemSoftwareEncoderMemory footprintNetwork topologyComputer architectureSoftware architectureDistributed computingTopology (electrical circuits)Operating systemEngineering

Abstract

fetched live from OpenAlex

Hardware software cosynthesis process tries to determine system architecture for an embedded application. In this paper, a new cosynthesis approach is presented, which targets distributed memory architectures for high performance embedded systems. The target embedded architecture consists of heterogeneous processing elements (PEs) with point- to-point communication structure. The main steps of the cosynthesis process include PE selection, pipelined task allocation and scheduling, and regular topology mapping. Initially, an irregular topology is generated and then mapped to regular topology architecture (e.g. mesh, hypercube and tree). The cosynthesis method is tested for the MPEG encoder application.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.873
Threshold uncertainty score0.656

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.015
GPT teacher head0.266
Teacher spread0.251 · 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 designBench or experimental
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

Citations1
Published2007
Admission routes1
Has abstractyes

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