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Record W2739725956

Simplifying system-on-chip design through architecture and system cad tools

2006· article· en· W2739725956 on OpenAlexafffund
Lesley Shannon

Bibliographic record

VenueTSpace · 2006
Typearticle
Languageen
FieldComputer Science
TopicInterconnection Networks and Systems
Canadian institutionsUniversity of Toronto
FundersUniversity of TorontoGovernment of OntarioIris O'Brien Foundation
KeywordsDatapathReconfigurabilityEmbedded systemComputer architectureApplication-specific integrated circuitField-programmable gate arraySystem on a chipReuseAbstraction layerComputer scienceFPGA prototypeIntegrated circuit designEngineeringSoftwareTelecommunications
DOInot available

Abstract

fetched live from OpenAlex

Historically designers created computing systems by combining Integrated Circuits (ICs) on Printed Circuit Boards (PCBs), whereas now they are able to form complete Systems-on-Chip (SoCs). For the purpose of this study, SoCs are defined as a collection of functional units on one chip that interact to perform a desired operation. These modules are typically of a coarse granularity to promote reuse of previously designed Intellectual Property (IP). The decreasing size of process technologies enables designers to implement increasingly complex SoCs using both Application Specific Integrated Circuits (ASICs) and Field Programmable Gate Arrays (FPGAs). The impact of increasing design complexity is increased design time and costs for electronics. Therefore, this research investigates methods to facilitate the design of SoCs through both architecture and CAD tools. This thesis has two main contributions. The first is an architectural framework for SoCs, wherein they are modelled as Systems Integrating Modules with Predefined Physical Links (SIMPPL). The strength of the model is the Computing Element (CE) abstraction that separates the module's datapath from system-level control and communications to facilitate design reuse. Although SIMPPL can be used to build SoCs for ASICs or FPGAs, using an FPGA provides designers with a reprogrammable implementation platform. Thus, our second contribution is to develop a design infrastructure that leverages the advantages of reconfigurability.

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: none
Teacher disagreement score0.976
Threshold uncertainty score0.675

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 science0.0000.000
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 head0.039
GPT teacher head0.275
Teacher spread0.235 · 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

Citations0
Published2006
Admission routes2
Has abstractyes

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