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

Borealis project presents an advanced concept of machine for powder deposition additive manufacturing and ablation processes that integrates 5 AM technologies in a unique solution. The machine is characterized by a redundant structures constituted by a large portal and a small PKM enabling the covering of a large range of working cube and a pattern of ejective nozzles and hybrid laser source targeting a deposition rate of 2000cm3/h with 30 sec set-up times. The machine is enriched with a software infrastructure which enable a persistent monitoring and in line adaptation of the process with zero scraps along with number of energy and resource efficiency optimization policies and harvesting systems which make the proposed solution the less environmental invasive in the current market. Borealis idea results from a consortium composed by the excellence of developers of worldwide recognized laser machines and advanced material processing together with the highly precision and flexible mechatronic designers. These two big clusters decided to join their expertise and focus on new manufacturing challenges coming from complex product machining in the field of aerospace, medtech and automotive represented by major partners in the market. Borealis project targets a TRL 6 and will provide as outcome of three years work two complete Borealis machine in two dimensions – a lab scale machine and a full size machine – which are foreseen to be translated into industrial solution by 2019.

Fluently leverages the latest advancements in AI-driven decision-making process to achieve true social collaboration between humans and machines while matching extremely dynamic manufacturing contexts. The project results will be: 1) Fluently Smart Interface unit and 2) the Robo-Gym. The Fluently Smart Interface unit features: 1) interpretation of speech content, speech tone and gestures, automatically translated into robot instructions, making industrial robots accessible to any skill profile; 2) assessment of the operator’s state through a dedicated sensors’ infrastructure that complements a persistent context awareness to enrich an AI-based behavioural framework in charge of triggering the generation of specific robot strategies; 3) modelling products and production changes in a way they could be recognized, interpreted and matched by robots in cooperation with humans. Robots equipped with Fluently will constantly embrace humans’ physical and cognitive loads, but will also learn and build experience with their human teammates to establish a manufacturing practise relying upon quality and wellbeing. The bond between human and robot is personalized, and it is established during a preliminary training at the Robo-Gym, the first European hub for human-robot interactive training, where human and robot learn from each other a common work practice. Fluently will be validated by demonstration on three full scale pilots characterized by various level of automation, production dynamism and complexity of the manufacturing decision making process. The total project value required to commercialize the Fluently device and Robo-Gym concept is 18.8 M EUR. Forecast for EBITDA in 2028 is 16.3 M EUR, payback in 2028, with 13% gross profit and ROI 91% in line with sectorial performances. The new turnover generated requires at least 325 workers employed along the Fluently supply chain. Fluently will bring together 21 key industrial and academic stakeholders from 13 countries.