An AI Factory Digital Twin Deployed Within a High Performance Edge Architecture
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F23%3A00372904" target="_blank" >RIV/68407700:21730/23:00372904 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/ICNP59255.2023.10355613" target="_blank" >https://doi.org/10.1109/ICNP59255.2023.10355613</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICNP59255.2023.10355613" target="_blank" >10.1109/ICNP59255.2023.10355613</a>
Alternative languages
Result language
angličtina
Original language name
An AI Factory Digital Twin Deployed Within a High Performance Edge Architecture
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
R - Projekt Ramcoveho programu EK
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2023 IEEE 31st International Conference on Network Protocols (ICNP)
ISBN
979-8-3503-0323-0
ISSN
1092-1648
e-ISSN
2643-3303
Number of pages
6
Pages from-to
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Publisher name
IEEE Xplore
Place of publication
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Event location
Reykjavik
Event date
Oct 10, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001139632900043