Cynergy4MIE is a visionary project poised to revolutionize Europe's industrial landscape by bridging the gap between foundational technology layers, cross-sectional technologies, and key application areas. The project addresses the pressing need for efficient resource utilization and synergy creation across ecosystems. By actively managing requirements from various key application areas, Cynergy4MIE aims to steer developments in foundational technology layers and cross-sectional technologies, enabling unparalleled collaboration and resource optimization. This approach promises faster time-to-market, efficient resource utilization, and enhanced technological exchange between key application areas. Cynergy4MIE's strategy aligns with the EU's agenda and emphasizes urgency, resilience, technological partnerships, and cross-domain integration to champion European competitiveness. The project's long-term impact rests on embracing urgency, fostering competitive resilience, strengthening technological partnerships, harnessing ecosystem synergies, promoting cross-domain integration, advancing AI competence, prioritizing sustainability, enhancing productivity, and ensuring user-centric digitalization while forming strategic alliances. Cynergy4MIE envisions a future where emergent cyber-physical systems serve human-centric needs, drive domain convergence, and secure Europe's position as a global technology leader.

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The ambition of PRYSTINE is to strengthen and to extend traditional core competencies of the European industry, research and universities in smart mobility and in particular the electronic component and systems and cyber-physical systems domains. PRYSTINE's target is to realize Fail-operational Urban Surround perceptION (FUSION) which is based on robust Radar and LiDAR sensor fusion and control functions in order to enable safe automated driving in urban and rural environments. Therefore, PRYSTINE's high-level goals are: 1. Enhanced reliability and performance, reduced cost and power of FUSION components 2. Dependable embedded control by co-integration of signal processing and AI approaches for FUSION 3. Optimized E/E architecture enabling FUSION-based automated vehicles 4. Fail-operational systems for urban and rural environments based on FUSION PRYSTINE will deliver (a) fail-operational sensor-fusion framework on component level, (b) dependable embedded E/E architectures, and (c) safety compliant integration of Artificial Intelligence (AI) approaches for object recognition, scene understanding, and decision making within automotive applications. The resulting reference FUSION hardware/software architectures and reliable components for autonomous systems will be validated in in 22 industrial demonstrators, such as: 1. Fail-operational autonomous driving platform 2. An electrical and highly automated commercial truck equipped with new FUSION components (such as LiDAR, Radar, camera systems, safety controllers) for advanced perception 3. Highly connected passenger car anticipating traffic situations 4. Sensor fusion in human-machine interfaces for fail-operational control transition in highly automated vehicles PRYSTINE’s well-balanced, value chain oriented consortium, is composed of 60 project partners from 14 different European and non-European countries, including leading automotive OEMs, semiconductor companies, technology partners, and research institutes.

Europe has a lack of intellectual property in integrating artificial intelligence (AI) into digital applications. This is critical since the automatization reached saturated levels and AI in digitisation is an accepted approach for the upcoming transformation of the European industry. The potential of AI in economy and society is by far not enough exploited. Potential users of AI are not sufficiently supported to facilitate the integration of AI into their applications. Enabling of performance, industry and humanity by AI for digitising industry is the key to push the AI revolution in Europe and step into the digital age. Existing services providing state of the art machine learning (ML) and artificial intelligence solutions are currently available in the cloud. In this project, we aim to transfer machine learning and AI from the cloud to the edge in manufacturing, mobility and robotics. To achieve these targets we connect factories, processes, and devices within digitised industry by utilizing ML and AI for human machine collaboration, change detection, and detection of abnormalities. Hence, we gain knowledge by using existing data and arrange them into a processable representation or collect new data. We use this knowledge to change the semantics and the logical layer with a distributed system intelligence for e.g. quality control, production optimization. In AI4DI, we define a 7-key-target-approach to evaluate the relevance of AI methods within digitised industry. Each key target represents a field of activity and the corresponding target at the same time, dividing systems into heterogenous and homogenous systems, and evolving a common AI method understanding for these systems as well as for human machine collaboration. Furthermore, we investigate, develop and apply AI tools for change detection and distributed system intelligence, and develop hardware and software modules as internet of things (IoT) devices for sensing, actuating, and connectivity processing.

The Chips JU project E2PackMan contributes to strengthen innovation and packaging production in Europe to improve the ecosystem for leading edge / advanced electronics. Integration of more functionality in smaller volume is still the major driver in microelectronics. In order to facilitate further miniaturization, assembly and packaging (A&P) is becoming of increasing importance for the value chain of a microelectronic product. To secure the future of electronic products "Made in Europe", we need a big push to strengthen innovation and production in the A&P sector. Thus E2PackMan will push materials research as well as innovative equipment and process developments. The focus of this research will be on catalyzing industrial production capabilities in Europe, both strengthening the production capabilities of the large semiconductor suppliers in Europe and creating a network that enables SMEs to produce their innovative devices in Europe. A world-class consortium of 60 partners from 13 European countries has been brought together to push advanced packaging in Europe towards heterogeneous system integration: The E2PackMan -EU project tackles a coherent approach that includes the whole value chain from material, process and technology developments validated by test-builds including the interface of the package to the chips and to the board/system.