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.

The increasing need for cloud services at the edge (edge–services) is caused by the rapidly growing quantity and capabilities of connected and interacting edge devices exchanging vast amounts of data. This poses different challenges to cloud computing architectures at the edge, such as i) ability to provide end-to-end transaction resiliency of applications broken down in distributions of microservices; ii) creating reliability and stability of automation in cloud management under increasing complexity iii) secure and timely handling of the increasing and latency sensitive flow (east-west) of sensitive data and applications; iv)need for explainable AI and transparency of the increasing automation in edge-services platform by operators, software developers and end-users. ACES will solve these challenges by infused autopoiesis and cognition on different levels of cloud management to empower with AI different functionalities such as: workload placement, service and resource management, data and policy management. ACES key outcomes will be: i) autopoiesis cognitive cloud-edge framework; ii) awareness tools, AI/ML agents for workload placement, service and resource management, data and policy management, telemetry and monitoring; iii) agents safeguarding stability in situations of extreme load and complexity; iv) swarm technology-based methodology and implementation for orchestration of resources in the edge; v) edge-wide workload placement and optimization service; vi) an app store for classification, storage, sharing and rating of AI models used in ACES. ACES will be demonstrated and validated in 3 scenarios demanding for support of highly decentralised computing, ability to take autonomic decisions, reducing costs of cloud-edge management and increasing their efficiency ,thus reducing impact on environment. To foster the uptake of ACES outcomes beyond its lifespan, different activities are foreseen to drive adoption to a wider network of stakeholders in key sectors

CAPE (European Open Compute Architecture for Powerful Edge) aims to redefine the landscape of edge-cloud computing infrastructures by developing the EdgeMicroDataCenters (EMDC's) and eHPS as a 'new unit of computing' for data-dense edge environments. The project designs and showcase an innovative, open hardware platform that is dynamically composable via CXL to answer the end user needs. EMDC and eHPS provides an open high density platform for heterogeneous computing units (XPU), RISC-V architectures all based on industry-standard form factor, COM-HPC that is supported by a robust ecosystem of Original Equipment Manufacturers (OEMs) within Europe, ensuring wide accessibility and adoption. To allow end users to be digital sovereign e.g. manage the governance of data, AI models, applications deployed across an ‘edge-first’ edge to cloud continuum, CAPE will employ a cloud-agnostic overlay known as Infrastructure from Code (IfC). This innovative approach abstracts the complexities inherent in diverse cloud computing infrastructures and services, empowering software developers to deploy applications effortlessly across the edge-to-cloud continuum. This is achieved without necessitating extensive knowledge of the underlying cloud infrastructure, enabling deployments across on-premise and off-premise, public and private cloud environments with minimal complexity. CAPE's solution will be validated in 3 use cases: the management of intelligent electric energy microgrids, edge AI and satellite communications. All usecases will evaluate RISC-V (EPI) and CXL solutions. Each usecase will be evaluated on technical, economical and sustainability aspects and benchmarked against legacy hardware in local clouds against edge-optimized data centers.

The “Multi-layer 360° dYnamic orchestration and interopeRable design environmenT for compute-continUum Systems (MYRTUS)” project aims to provide the technology to make CPS evolving towards a living dimension, embracing the technological principles of the TransContinuum Initiative where “edge, fog and cloud computing platforms are being pulled closer together into a seamless execution environment” and “programming has to be reinvented, with languages and tools to orchestrate collaborative distributed and decentralised components, as well as components augmented with interface contracts covering both functional and non-functional properties”. MYRTUS puts together different technologies to design and operate complex, distributed, and heterogeneous CPS computing infrastructures, leveraging diverse cloud to edge technologies, including from off the shelf CPUs to custom AI accelerators, offered by multiple providers. Main MYRTUS results are (i) the MYRTUS reference infrastructure, comprising a diversity of heterogeneous, autonomous, federated and collaborative computing nodes distributed across the computing continuum; (ii) a novel management scheme featuring MIRTO, an AI-powered cognitive engine capable of orchestrating, at 360°, the whole continuum infrastructure, and (iii) the MYRTUS design and programming environment to design, deploy, and operate such complex infrastructures. MYRTUS solutions represent the instruments to unlock the new living dimension of CPS, pursuing also sustainable and responsible computing, openness, security and trustworthiness, and promoting strategic industrial cooperations by establishing synergies with relevant initiatives and projects, as IPCEI, Gaia-X, TransContinuum Initiative. Technology assessment is carried out within two challenging scenarios, Healthcare and Mobility, involving humans.

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.