An AI Factory Digital Twin Deployed Within a High Performance Edge Architecture

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F02673975%3A_____%2F23%3AN0000002" target="_blank" >RIV/02673975:_____/23:N0000002 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/10355613" target="_blank" >https://ieeexplore.ieee.org/document/10355613</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICNP59255.2023.10355613" target="_blank" >10.1109/ICNP59255.2023.10355613</a>

Jazyk výsledku

angličtina

Název v původním jazyce

An AI Factory Digital Twin Deployed Within a High Performance Edge Architecture

Popis výsledku v původním jazyce

The exponential proliferation of big data and computation-intensive tasks, such as Artificial Intelligence (AI) applications in factories, poses a significant challenge for the current datacenter-focused technological architecture. The ”Big data pRocessing and Artificial Intelligence at the Network Edge” (BRAINE) project addresses this problem by introducing an innovative system architecture designed explicitly for compute intensive edge deployments. BRAINE focuses on decentralizing the computation tasks, enabling a significant reduction in latency, and optimizing the placement of applications within a cloud-edge continuum to ensure optimal operational efficiency. This paper presents the design, implementation, and testing of our novel system architecture in the context of an AI digital twin for factory robotics. Our empirical results indicate substantial improvements in performance metrics such as processing speed and latency compared to traditional architectures and approaches.

Název v anglickém jazyce

An AI Factory Digital Twin Deployed Within a High Performance Edge Architecture

Popis výsledku anglicky

The exponential proliferation of big data and computation-intensive tasks, such as Artificial Intelligence (AI) applications in factories, poses a significant challenge for the current datacenter-focused technological architecture. The ”Big data pRocessing and Artificial Intelligence at the Network Edge” (BRAINE) project addresses this problem by introducing an innovative system architecture designed explicitly for compute intensive edge deployments. BRAINE focuses on decentralizing the computation tasks, enabling a significant reduction in latency, and optimizing the placement of applications within a cloud-edge continuum to ensure optimal operational efficiency. This paper presents the design, implementation, and testing of our novel system architecture in the context of an AI digital twin for factory robotics. Our empirical results indicate substantial improvements in performance metrics such as processing speed and latency compared to traditional architectures and approaches.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20202 - Communication engineering and systems

Projekt

<a href="/cs/project/8A20004" target="_blank" >8A20004: Big data pRocessing and Artificial Intelligence at the Network Edge</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2023 IEEE 31st International Conference on Network Protocols (ICNP)

ISBN

979-8-3503-0322-3

ISSN

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e-ISSN

2643-3303

Počet stran výsledku

6

Strana od-do

—

Název nakladatele

—

Místo vydání

—

Místo konání akce

Reykjavik, Iceland

Datum konání akce

10. 10. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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