CAPE is a European-funded project targeting to reshape edge-cloud computing by defining edge micro data centers as a new unit of computing. Fully committing to open source, CAPE develops a fully Composable Infrastructure (CI) for high-performance edge server hardware platforms grounded in open, forward-looking standards. Together with an open-source software stack covering the Edge-Cloud Continuum, this holistic approach boosts power and energy efficiency while reducing resource overprovisioning. Completely based on open standards, CAPE strengthens the digital sovereignty Europe needs in a challenging future. This work gives an overview of the current architectural blueprint of the project, focusing on integrating game-changing technologies like Compute Express Link (CXL) for compute and memory disaggregation, pushing open source cluster management, and AI-assisted deployment software stacks using Infrastructure from Code (IfC). The proposed approaches and benefits for future Edge-Cloud data centers are demonstrated within three use cases, ranging from Smart Grid and Edge-AI to Satellite Data Processing.

The VEDLIoT project aims to develop energy-efficient Deep Learning methodologies for distributed Artificial Intelligence of Things (AIoT) applications. During our project, we propose a holistic approach that focuses on optimizing algorithms while addressing safety and security challenges inherent to AIoT systems. The foundation of this approach lies in a modular and scalable cognitive IoT hardware platform, which leverages microserver technology to enable users to configure the hardware to meet the requirements of a diverse array of applications. Heterogeneous computing is used to boost performance and energy efficiency. In addition, the full spectrum of hardware accelerators is integrated, providing specialized ASICs as well as FPGAs for reconfigurable computing. The project's contributions span across trusted computing, remote attestation, and secure execution environments, with the ultimate goal of facilitating the design and deployment of robust and efficient AIoT systems. The overall architecture is validated on use-cases ranging from Smart Home to Automotive and Industrial IoT appliances. Ten additional use cases are integrated via an open call, broadening the range of application areas.

Within VEDLIoT, a project targeting the development of energy-efficient Deep Learning for distributed AIoT applications, several accelerator platforms based on technologies like CPUs, embedded GPUs, FPGAs, or specialized ASICs are evaluated. The VEDLIoT approach is based on modular and scalable cognitive IoT hardware platforms. Modular microserver technology enables the integration of different, heterogeneous accelerators into one platform. Benchmarking of the different accelerators takes into account performance, energy efficiency and accuracy. The results in this paper provide a solid overview regarding available accelerator solutions and provide guidance for hardware selection for AIoT applications from far edge to cloud. VEDLIoT is an H2020 EU project which started in November 2020. It is currently in an intermediate stage. The focus is on the considerations of the performance and energy efficiency of hardware accelerators. Apart from the hardware and accelerator focus presented in this paper, the project also covers toolchain, security and safety aspects. The resulting technology is tested on a wide range of AIoT applications.

The VEDLIoT project targets the development of energy-efficient Deep Learning for distributed AIoT applications. A holistic approach is used to optimize algorithms while also dealing with safety and security challenges. The approach is based on a modular and scalable cognitive IoT hardware platform. Using modular microserver technology enables the user to configure the hardware to satisfy a wide range of applications. VEDLIoT offers a complete design flow for Next-Generation IoT devices required for collaboratively solving complex Deep Learning applications across distributed systems. The methods are tested on various use-cases ranging from Smart Home to Automotive and Industrial IoT appliances. VEDLIoT is an H2020 EU project which started in November 2020. It is currently in an intermediate stage with the first results available.

The LEGaTO project leverages task-based programming models to provide a software ecosystem for Made in-Europe heterogeneous hardware composed of CPUs, GPUs, FPGAs and dataflow engines. The aim is to attain one order of magnitude energy savings from the edge to the converged cloud/HPC, balanced with the security and resilience challenges. LEGaTO is an ongoing three-year EU H2020 project started in December 2017.

Big data and machine learning applications are posing steadily increasing challenges to the used compute platforms in terms of performance and energy efficiency. In this paper we utilize the highly scalable heterogeneous server platform RECS for evaluation of a wide variety of hardware platforms ranging from general purpose CPUs via ARM-based SoCs to GPGPUs and FPGAs. The self-organizing map, a popular neural network model for unsupervised clustering and dimensionality reduction, is used as a typical example for machine learning applications in the big data domain. Optimized implementations of the algorithm have been developed for each of the target architectures. An in-depth analysis of the achieved performance and energy efficiency for a wide variety of application parameters shows that no single architecture performs best in terms of energy efficiency for the complete design space. In our study, ARM-based SoCs achieved the highest efficiency for small network sizes while FPGAs and GPGPUs perform best for large data sets. Compared to an implementation based on the Matlab SOM toolbox, our optimized multi-threaded CPU implementation achieves two orders of magnitude higher performance and energy efficiency. Large simulations especially benefit from the FPGA implementation, which outperforms the optimized CPU implementation by a factor of 220 and provides a 28-times higher energy efficiency.

The Modular Microserver DataCentre (M2DC) project will investigate, develop and demonstrate a modular, highly-efficient, cost-optimized server architecture composed of heterogeneous microserver computing resources, being able to be tailored to meet requirements from various application domains such as image processing, cloud computing or HPC. M2DC will be built on three main pillars: a flexible server architecture that can be easily customised, maintained and updated, advanced management strategies and system efficiency enhancements (SEE), well-defined interfaces to surrounding software data centre ecosystem.

Increasing the energy efficiency of today's high-performance computing systems requires new approaches that go beyond homogeneous architectures, which primarily target maximum performance per node. Heterogeneous architectures that can be tailored towards the specific needs of a particular application are a promising alternative to state-of-the-art server systems. In this paper, we present a novel highly-scalable server architecture that seamlessly integrates variable combinations of general purpose CPUs, embedded CPUs, FPGAs, and GPUs. Embedded CPUs based on the latest ARM Cortex-A15 devices with integrated embedded GPUs are combined with FPGA-based reconfigurable SoCs, which can be used for application-specific hardware acceleration. A dedicated monitoring network enables continuous control and fine-grained observation of all relevant system parameters. Communication between the compute nodes is established by a flexible multi-level interconnect that can be adapted to various Ethernet and Infiniband standards. The communication facilities are further enhanced by direct high-bandwidth, low-latency links between the embedded FPGA-based reconfigurable SoCs.