VERGE will tackle evolution of edge computing from three perspectives: “Edge for AI”, “AI for Edge” and security, privacy and trustworthiness of AI for Edge. “Edge for AI” defines a flexible, modular and converged Edge platform that is ready to support distributed AI at the edge. This is achieved by unifying lifecycle management and closed-loop automation for cloud-native applications, MEC and network services, while fully exploiting multi-core and multi-accelerator capabilities for ultra-high computational performance. “AI for Edge” enables dynamic function placement by managing and orchestrating the underlying physical, network, and compute resources. Application-specific network and computational KPIs will be assured in an efficient and collision-free manner, taking Edge resource constraints in to account. Security, privacy and trustworthiness of AI for Edge are addressed to ensure security of the AI-based models against adversarial attacks, privacy of data and models, and transparency in training and execution by providing explanations for model decisions improving trust in models. VERGE will verify the three perspectives through delivery of 7 demonstrations across two use cases - XR-driven Edge-enabled industrial B5G applications across two separate Arçelik sites in Turkey, and Edge-assisted Autonomous Tram operation in Florence. VERGE will disseminate results to academia, industry and the wider stakeholder community through liaisons and contributions to relevant standardization bodies and open sources, a series of demonstrations showing progression through TRLs and by creating an open dataspace for enabling public access to the datasets generated by the project.

Healthcare is the fasted growing EU27 expenditure. Personalised medicine, comprising tailored approaches for prevention, diagnosis, monitoring and treatment is essential to reduce the burden of disease and improve the quality of life. Integration of multiple data types (multimodal data) into artificial intelligence models is required for the development of accurate and personalised interventions. This is particularly true for the inclusion of genomic data, which is information-rich and individual-specific, and more routinely available as the cost of sequencing continues to fall. Multimodal data integration is complex due to privacy & governance requirements, the presence of multiple standards, distinct data formats, and underlying data complexity and volume. NextGen tools will remove barriers in data integration several cardiovascular use cases. NextGen deliverables will include tooling for multimodal data integration and research portability, extension of secure federated analytics to genomic computation, more effective federated learning over distributed infrastructures, more effective and accessible tools for genomic data analysis; improved clinical efficiency of variant prioritisation; scalable genomic data curation; and improved data discoverability and data management. A comprehensive gap analysis of the existing landscape, factoring ongoing initiatives will ensure NextGen deliverables are forward-looking and complementary. NextGen embedded governance framework and robust regulatory processes will ensure secure multi-jurisdictional multiomic multimodal data access aligned with initiatives including “1+ Million Genomes” and the European Health Data Space. Several real-world pilots will demonstrate the effectiveness of NextGen tools and will be integrated in the NextGen Pathfinder network of five collaborating clinical sites as a self-contained data ecosystem and comprehensive proof of concept.

From edge to cloud, big data analytics is growing fast, and its energy consumption has become a reason of concern for national grids and they generate significant carbon emissions. The GLACIATION project aims to address this issue through energy-efficient data operations. By developing a novel Distributed Knowledge Graph (DKG) that stretches across the edge-core-cloud architecture, reduction in the energy consumption for data processing will be achieved through AI enforced minimal data movement operations. GLACIATION will achieve significant power consumption reduction through optimizing the location where analytics are carried out. The projects Meta Data framework will provide tools that incorporate privacy and trust aspects in the data operations. GLACIATION is demonstrated on three relevant industry settings which benefit from optimized data movement and power consumption reduction. More specifically, GLACIATION use cases cover public-service, manufacturing, and enterprise data analytics.

SHIFT2DC project aims to propose and implement a top-down application-agnostic approach for the design, simulation, test, validation, and application of both medium (MV) and low voltage (LV) direct current (DC) solutions. Thirty-two partners, including affiliated and associated partners, from twelve countries, will join expertise to develop, test and demonstrate the technical feasibility, cost-benefit, life cycle and environmental impact of the proposed DC solutions in Data centres, Buildings, Industry and Ports across Europe (Germany, France, and Portugal). The field-tests and demonstrators will allow an evaluation of the advanced control methodologies and tools, the definition of the appropriate implementation conditions, and a consolidation of the most promising solutions and corresponding business models for MV and LV DC systems. Beyond the DC solutions that will be tested and demonstrated, SHIFT2DC will also evaluate the consumers´perspective regarding DC solutions and propose new tools that promote the faster adoption of DC solutions. All the solutions and tools that will be developed in the SHIFT2DC project will be case-agnostic allowing the use of its results in most applications. In a second stage of the development, specific libraries for buildings, datacentres, industry and ports will be proposed allowing a better and more detailed simulation of the mentioned environments. The DC solutions proposed and developed in the SHIFT2DC project will be designed taking into consideration the interoperability requirements, the scalability opportunities and the security and privacy needs. In addition, the project results will contribute to the development of standards in compliance with the needs of DC solutions. Finally, a regulatory framework that promotes the adoption of MVDC and LVDC solutions and assures secure and economic power systems management under hybrid AC/DC grids will be proposed.

The wide-spread adoption of AI and analytics has resulted in a rapidly expanding market for novel hardware accelerators that can provide energy-efficient scaling of training and inference tasks at both the cloud and edge. Unfortunately, all popular solutions AI acceleration solutions today use proprietary, closed hardware—software stacks, leading to a monopolization of the AI acceleration market by a few large industry players. The vision of SYCLOPS project is to enable better solutions for AI/data mining for extremely large and diverse data by democratizing AI acceleration using open standards, and enabling a healthy, competitive, innovation-driven ecosystem for Europe and beyond. This vision relies on the convergence of two important trends in the industry: (i) the standardization and adoption of RISC-V, a free, open Instruction Set Architecture (ISA), for AI and analytics acceleration, and (ii) the emergence and growth of SYCL as a cross-vendor, cross-architecture, data parallel programming model for all types of accelerators, including RISC-V. The goal of project SYCLOPS is to bring together these standards for the first time in order to (i) demonstrate ground-breaking advances in performance and scalability of extreme data analytics using a standards-based, fully-open, AI acceleration approach and (ii) enable the development of inter-operable (open and vendor neutral interfaces/APIs), trustworthy (verifiable and standards-based hardware/software), and green (via application-specific processor customization) AI systems. In doing so, we will use the experience gained in SYCLOPS to contribute back to SYCL and RISC-V standards and foster links to respective academic, industrial and innovator communities (RISC-V foundation, EPI, Khronos, ISO C++). Bringing together the two standards enables codesign in both standards, which in turn, will enable a broader AI accelerator design space, and a richer ecosystem of solutions.