DAIS – Distributed Artificial Intelligent System, aims at creating an intelligence centred heterogeneous distributed edge computing systems and solutions. DAIS approach is to develop intelligent, secure, and trustworthy systems for industrial applications to provide comprehensive cost and energy-efficient solutions of intelligent, end-to-end secure, trustworthy connectivity and interoperability to bring the Internet of Things and Artificial Intelligence together.
About the project
The DAIS project will research, promote, and deliver distributed artificial intelligence (AI) systems, as well as respective architectures, solving the problems of running existing algorithms on these vastly distributed edge devices. DAIS is organised around eight different supply chains: five focus on delivering the hardware and software that is needed to run industrial grade AI on different type of networking topologies, while three others will demonstrate how known AI challenges, from different functional areas, are met by this pan European effort.
DAIS will establish the EU as a centre for intelligent, secure, and trustworthy systems for industrial applications enabled by a strong industry with a good reputation and an informed society ensuring AI based products and services to comply with European values and proudly being “Made in Europe”.
Advance in Edge Computing and AI applications has pushed Edge Intelligence (EI) to the horizon, making DAIS approach to develop intelligent, secure and trustworthy systems for industrial applications to provide comprehensive cost and energy-efficient solutions of intelligent, end-to-end secure, trustworthy connectivity and interoperability to bring the Internet of Things and Artificial Intelligence together.
Providing intelligent processing of data and communication locally at the edge to enable real-time and safety-critical industrial applications.
Developing industrial-grade secure, safe, and reliable solutions that can cope with cyberattacks and difficult network conditions.
Providing AI techniques on the edge that match with diverse computing powers contrary to relatively consistent computing power on the cloud. As different AI algorithms have different computing power requirements, it is a big challenge to match an existing algorithm with the certain edge platform.
Distribute and divide the complex AI operations between the cloud and edge, with edge undertaking early intelligent data processing reducing the bandwidth of data being transmitted to cloud; and building the hardware and software infrastructure to provide for this in Europe.
Providing data sharing and collaborating solutions on the edge to handle the temporal-spatial diversity of edge data.
Developing solutions for IoT, i.e. mostly wireless devices with energy- and processing- constraints, in heterogeneous and also hostile/harsh environments.
Providing re-usable solutions across industrial domains.
Methodological approach with the Integral Supply Chain, from academic, to system designers and integrators, to component providers, applications and services developers & providers and end users.