In recent years, we have witnessed an explosion of artificial intelligence (AI) applications which will continue to grow over the next decade. An intelligent and digitized society will be ubiquitous, enabled by increased advances in nanoelectronics. Key drivers will be sensors interfacing with the physical world and taking appropriate action in a timely manner while operating with energy efficiency and flexibility to adapt. The vast majority of sensors receive analog inputs from the real world and generate analog signals to be processed. However, digitizing these signals not only creates enormous amount of raw data but also require a lot of memory and high-power consumption. As the number of sensor-based IoTs grows, bandwidth limitations make it difficult to send everything back to a cloud rapidly enough for real-time processing and decision-making, especially for delay-sensitive applications such as driverless vehicles, robotics, or industrial manufacturing. In this context, PHASTRAC proposes to develop a novel analog-to-information neuromorphic computing paradigm based on oscillatory neural networks (ONNs). We propose a first-of-its-kind and novel analog ONN computing architecture to seamlessly interface with sensors and process their analog data without any analog-to-digital conversion. ONNs are biologically inspired neuromorphic computing architecture, where neuron oscillatory behavior will be developed by innovative phase change VO2 material coupled with synapses to be developed by bilayer Mo/HfO2 RRAM devices. PHASTRAC will address key issues 1) novel devices for implementing ONN architecture, 2) novel ONN architecture to allow analog sensor data processing, and 3) processing the data efficiently to take appropriate action. This “sensing-to-action” computing approach based on ONN technology will allow energy efficiency improvement 100x-1000x and establish a novel analog computing paradigm for improved future human-machine interactions.

The principal aim of the project is to develop an EU-centric supply base for key automotive PEM fuel cell components that achieve high power density and with volume production capability along with embedded quality control as a key focus - to enable the establishment of a mature Automotive PEM fuel cell manufacturing capability in Europe. It will exploit existing EU value adding competencies and skill sets to enhance EU employment opportunities and competitiveness while supporting CO2 reduction and emissions reduction targets across the Transport sector with increased security of fuel supply (by utilising locally produced Hydrogen).

CRESCENDO will develop highly active and long-term stable electrocatalysts of non-platinum group metal (non-PGM) catalysts for the PEMFC cathode using a range of complementary and convergent approaches, and will re-design the cathode catalyst layer so as to reach the project target power density and durability requirements of 0.42 W/cm2 at 0.7 V, and 1000 h with less than 30% performance loss at 1.5 A/cm2 after 1000 h under the FC-DLC, initially in small and ultimately full-size single cells tested in an industrial environment on an industrially scaled-up catalyst. The proposal includes the goal of developing non-PGM or ultra-low PGM anode catalysts with greater tolerance to impurities than current low Pt-loaded anodes. It will develop and apply advanced diagnostics methods and tests, and characterisation tools for determination of active site density and to better understand performance degradation and mass transport losses. The proposal builds on previous achievements in non-PGM catalyst development within all of the university and research organisation project partners. It benefits from the unrivalled know-how in catalyst layer development at JMFC and the overarching expertise at BMW in cell and stack testing, and in guiding the materials development to align with systems requirements.

The next generation of networked embedded systems (ES) necessitates rapid prototyping and high performance while maintaining key qualities like trustworthiness and safety. However, deployment of safety-critical ES suffers from complex software (SW) toolchains and engineering processes. Moreover, the current trend in autonomous systems relying on Machine Learning (ML) and AI applications in combination with fail-operational requirements renders the Verification and Validation (V&V) of these new systems a challenging endeavor. Prime examples are autonomous driving cars that are prone to various safety/security vulnerabilities. The XANDAR project is built to exactly match the goals defined within the ICT-50 Software Technologies. XANDAR will deliver a mature SW toolchain (from requirements capture down to the actual code integration on target including V&V) fulfilling the needs of the industry for rapid prototyping of interoperable and autonomous ES. Starting from a model-based system architecture, XANDAR will leverage novel automatic model synthesis and software parallelization techniques to achieve specific non-functional requirements setting the foundation for a novel real-time, safety-, and security-by-Construction (X-by-Construction) paradigm. For the first time, XbC-guided code generation for non-deterministic ML/AI applications will be combined with novel runtime monitors to ensure fail-operation in the presence of runtime faults and security exploitations. The project provides a consortium covering the full spectrum of ES and software engineering. XANDAR will be validated by an automotive OEM (BMW) and the German Aerospace Center (DLR). Leading European SMEs and enterprises such as Vector, AVN, and fentISS as well as successful academic partners will contribute their diverse knowhow in Model-Driven Engineering, Software Systems and V&V, multicore architectures, code generation, and security enforcements from higher-level behavioral models to actual runnables.