no code implementations • 9 May 2023 • Kevin Mika, René Griessl, Nils Kucza, Florian Porrmann, Martin Kaiser, Lennart Tigges, Jens Hagemeyer, Pedro Trancoso, Muhammad Waqar Azhar, Fareed Qararyah, Stavroula Zouzoula, Jämes Ménétrey, Marcelo Pasin, Pascal Felber, Carina Marcus, Oliver Brunnegard, Olof Eriksson, Hans Salomonsson, Daniel Ödman, Andreas Ask, Antonio Casimiro, Alysson Bessani, Tiago Carvalho, Karol Gugala, Piotr Zierhoffer, Grzegorz Latosinski, Marco Tassemeier, Mario Porrmann, Hans-Martin Heyn, Eric Knauss, Yufei Mao, Franz Meierhöfer
The VEDLIoT project aims to develop energy-efficient Deep Learning methodologies for distributed Artificial Intelligence of Things (AIoT) applications.
An industry of data and annotation services has emerged to serve the development of such data-intensive automotive software components.
Especially the training data used during the development of ML models have major influences on the later behaviour of the system.
In this paper, we propose to extend the state of the art on architecture framework by providing a mathematical model for system architectures, which is scalable and supports co-evolution of different aspects for example of an AI system.
[Principal ideas/results] Based on qualitative analysis of data from semi-structured interviews, the case study shows that there is a lack of standardisation for context definitions across the industry, ambiguities in the processes that lead to deriving the ODD, missing documentation of assumptions about the operational context, and a lack of involvement of function developers in the context definition.
Availability of powerful computation and communication technology as well as advances in artificial intelligence enable a new generation of complex, AI-intense systems and applications.