MOSAIC addresses a grand challenge for European competitiveness: technological independence and filled fabs in the landscape of automated systems. By fostering innovation in Electronic Components and Systems (ECS), MOSAIC aims to propel Europe to excellence and digital autonomy, directly linked to the EU Chips Act. The project achieves this through a comprehensive strategy. It will develop next-generation ECS offering superior, cognitive system intelligence, enabling energy efficiency and robustness. These results will be tailored to the demands of automated systems, enabling rapid data processing and intuitive, AI-enabled decision- making. MOSAIC tackles the challenge of integrating diverse perception hardware configurations, ensuring that automated systems can perceive their surroundings in a non-invasive manner, avoiding a single point of failure, with unparalleled accuracy and decreased complexity. Additionally, the project emphasizes standardized communication protocols and interoperability, fostering a collaborative ecosystem across several industries, namely automotive, aerospace, maritime, industrial automation and infrastructures. By spearheading such advancements, MOSAIC empowers European ECS manufacturers to gain a competitive advantage. The project's achievements will be demonstrated in 31 cutting-edge technical showcases, indicatively global perception through 360° distributed radar, AI-enabled reasoning through magnetic field signature and resilient communications by means of non-terrestrial networks. 2 accompanying impact studies, will solidify Europe's position as a global leader in automated systems. MOSAIC leverages a pan-European consortium encompassing the entire ECS value chain, ensuring a comprehensive effort towards filling the European fabs and ensuring digital sovereignty. In essence, MOSAIC is an investment in Europe's future – a secure digital future of technological leadership, economic prosperity, and strategic independence.

EdgeAI is as a key initiative for the European digital transition towards intelligent processing solutions at the edge. EdgeAI will develop new electronic components and systems, processing architectures, connectivity, software, algorithms, and middleware through the combination of microelectronics, AI, embedded systems, and edge computing. EdgeAI will ensure that Europe has the necessary tools, skills, and technologies to enable edge AI as a viable alternative deployment option to legacy centralised solutions, unlocking the potential of ubiquitous AI deployment, with the long-term objective of Europe taking the lead of Intelligent Edge. EdgeAI will contribute to the Green Deal twin transition with a systemic, cross-sectoral approach, and will deliver enhanced AI-based electronic components and systems, edge processing platforms, AI frameworks and middleware. It will develop methodologies to ease, advance and tailor the design of edge AI technologies by co-ordinating efforts across 48 of the brightest and best R&D organizations across Europe. It will demonstrate the applicability of the developed approaches across a variety of vertical solutions, considering security, trust, and energy efficiency demands inherent in each of these use cases. EdgeAI will significantly contribute to the grand societal challenge to increase the intelligent processing capabilities at the edge.

Independent validation is fundamental to emphasise the capability and safety of any solution in the electric, connected and automated (ECA) vehicles space. It is vital that appropriate and audited testing takes place in a controlled environment before any deployment takes place. As the software and hardware components come from multiple vendors and integrate in numerous ways, the various levels of validation required must be fully understood and integration with primary and secondary parts must be considered. The key targets of ArchitectECA2030 are the robust mission-validated traceable design of electronic components and systems (ECS), the quantification of an accepted residual risk of ECS for ECA vehicles to enable type approval, and an increased end-user acceptance due to more reliable and robust ECS. The proposed methods include automatic built-in safety measures in the electronic circuit design, accelerated testing, residual risk quantification, virtual validation, and multi-physical and stochastic simulations. The project will implement a unique in-vehicle monitoring device able to measure the health status and degradation of the functional electronics empowering model-based safety prediction, fault diagnosis, and anomaly detection. A validation framework comprised of harmonized methods and tools able to handle quantification of residual risks using data different sources (e.g. monitoring devices, sensor/actuators, fleet observations) is provided to ultimately design safe, secure, and reliable ECA vehicles with a well-defined, quantified, and acceptable residual risk across all ECS levels. The project brings together stakeholders from ECS industry, standardization and certification bodies (e.g. ISO, NIST, TUEV), test field operators, insurance companies, and academia closely interacting with ECSEL lighthouse initiative Mobility.E to align and influence emerging standards and validation procedures for ECA vehicles.

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.