Deep Learning (DL) algorithms are an extremely promising instrument in artificial intelligence, achieving very high performance in numerous recognition, identification, and classification tasks. To foster their pervasive adoption in a vast scope of new applications and markets, a step forward is needed towards the implementation of the on-line classification task (called inference) on low-power embedded systems, enabling a shift to the edge computing paradigm. Nevertheless, when DL is moved at the edge, severe performance requirements must coexist with tight constraints in terms of power/energy consumption, posing the need for parallel and energy-efficient heterogeneous computing platforms. Unfortunately, programming for this kind of architectures requires advanced skills and significant effort, also considering that DL algorithms are designed to improve precision, without considering the limitations of the device that will execute the inference. Thus, the deployment of DL algorithms on heterogeneous architectures is often unaffordable for SMEs and midcaps without adequate support from software development tools. The main goal of ALOHA is to facilitate implementation of DL on heterogeneous low-energy computing platforms. To this aim, the project will develop a software development tool flow, automating: • algorithm design and analysis; • porting of the inference tasks to heterogeneous embedded architectures, with optimized mapping and scheduling; • implementation of middleware and primitives controlling the target platform, to optimize power and energy savings. During the development of the ALOHA tool flow, several main features will be addressed, such as architecture-awareness (the features of the embedded architecture will be considered starting from the algorithm design), adaptivity, security, productivity, and extensibility. ALOHA will be assessed over three different use-cases, involving surveillance, smart industry automation, and medical application domains

Manufacturing and logistics companies are subject to unforeseen events that disrupt the supply chain, causing production slowdowns, reduced output, and increased costs, making it difficult to meet customer demand. To mitigate these risks, manufacturers must build resilience across entire value chains. NARRATE will develop a sophisticated tool using AI, Digital Twin, and IoT technologies allowing end-to-end visibility and control over supply chain operations to monitor and predict potential disruptions, enabling supply chains to achieve improved resilience. The Intelligent Manufacturing Custodian (IMC) will leverage data from various production sources to enable proactive decision-making and act as a nerve centre for a supply-chain network, providing real-time monitoring and coordination of intelligent production processes and logistics. Integrating an IMC into a supply-chain will evolve its operations into Smart Manufacturing Network (SMN): a connected and self-orchestrated ecosystem linked end-to-end with programmable Manufacturing-as-a-Service capabilities that can withstand disruptions. A Digital Twin will provide a reliable model to represent production and operational data of an SMN to unlock deeper IMC intelligence. Collected data will train machine learning models to predict potential disruptions, such as natural disasters or delayed shipments. AI algorithms will analyse the data and provide real-time reporting and visualization on a dashboard. The IMC and Digital Twin interaction will generate powerful insights and self-adapting abilities that support an SMN to evolve under human supervision by switching services between multiple external partners to respond to risks and disruptions and improve energy efficiency, product circularity and environmental sustainability across the entire production process. The effectiveness of NARRATE will be evaluated by testing the IMC in real production environments in quite diverse industry sectors.

For the purpose of creating digitalisation and automation solutions Arrowhead Tools adresses engineering methodologies and suitable integrated tool chains. With the global aim of substantial reduction of the engineering costs for digitalisation/automation solutions. Thus the Arrowhead Tools vision is: - Engineering processes and tool chains for cost efficient developments of digitalization, connectivity and automation systems solutions in various fields of application For the further and wider commercialisation of automation and digitalisation services and products based on SOA, Arrowhead Framework and similar technologies there is a clear need for engineerings tools that integrates existing automation and digitalisation engineering procedures and tool with SOA based automation/digitalisation technology. For this purpose the Arrowhead Tool’s grand challenges are defined as: - Engineering costs reduction by 40-60% for a wide range of automation/digitalisation solutions. - Tools chains for digitalisation and automation engineering and management, adapted to: 1. existing automation and digitalisation engineering methodologies and tools 2. new IoT and SoS automation and digitalisation engineering and management tools 3. security management tools - Training material and kits for professional engineers The results will create impact on: - Automation and digitalisation solution market - Automation and engineering efficiency and the SSBS market - Automation and digitalisation security - Competence development on engineering of automation and digitalisation solution

The INDIGO-DataCloud project (INDIGO for short) aims at developing a data/computing platform targeted at scientific communities, deployable on multiple hardware, and provisioned over hybrid (private or public) e-infrastructures. This platform will be built by leading European developers, resource providers, e-infrastructures and scientific communities in order to ensure its successful exploitation and sustainability. All members of the consortium share the common interest in developing advanced middleware to sustain the deployment of service models and user tools to tackle the challenges of the Big Data era. INDIGO will exploit the formidable know-how that was built in Europe along the past ten years of collaborations on scientific computing based on different consolidated and emerging paradigms (HPC, Grid and Cloud). Regarding Cloud computing, both the public and private sectors are already offering IaaS-type Cloud resources. However, numerous areas are of interest to scientific communities where Cloud computing uptake is currently lacking, especially at the PaaS and SaaS levels. The project therefore aims at developing tools and platforms based on open source solutions addressing scientific challenges in the Cloud computing, storage and network areas. INDIGO will allow application development and execution on Cloud and Grid based infrastructures, as well as on HPC clusters. The project will extend existing PaaS solutions, allowing public and private e-infrastructures, including those provided by EGI, EUDAT, PRACE and HelixNebula, to integrate their existing services, make them available through GEANT-compliant federated and distributed AA policies, guaranteeing transparency and trust in the provisioning of such services. INDIGO will also address the development of a flexible and modular presentation layer connected to the expanded PaaS and SaaS frameworks developed by the project and allowing innovative user experiences, also from mobile appliances.