Project description
Artificial intelligence boosts high-performance computing used in safety-critical systems
High-performance computing that employs commercial off-the-shelf components offers an alternative path to increasing the computational capability of safety-critical applications. Despite their potential in a number of domains, use of these systems is limited due to the lack of certified, reliable hardware platforms. The EU-funded SELENE project aims to change this by proposing a safety-critical cognitive computing platform (CCP) with self-aware and self-adaptive capabilities. SELENE’s CCP uses artificial intelligence techniques to maximise the efficiency of the safety-critical system and adapt its behaviour in different domains such as automotive, space, avionics, robotics and factory automation.
Objective
Existing HW/SW platforms for safety-critical systems suffer from limited performance and/or from lack flexibility due to building on specific proprietary components, which jeopardize their wide deployment across domains. While some research attempts have been done to overcome some of these limitations, their degree of success has been low due to missing flexibility and extensibility, which would ensure that industry can take that path, as many industries need technologies on which they can rely during decades (e.g. avionics, space, automotive).
A number of high-performance computing (HPC) commercial off-the-shelf (COTS) platforms offer the computation capabilities needed by autonomous systems in domains such as automotive, space, avionics, robotics and factory automation by means of multicores, GPUs and other accelerators. Unfortunately, the utilization of HPC platforms has been traditionally considered out of the reach of the safety critical systems industry due to the difficulties or roadblocks these platforms bring to the certification process.
SELENE follows a radically new approach and proposes a Safety-critical Cognitive Computing Platform (CCP) with self-awareness, self-adapting, and autonomous capabilities. SELENE’s CCP uses artificial intelligence (AI) techniques to adapt the system to the particular internal and external (environmental) conditions with the aim of maximizing the efficiency of the system being able at the same time of meeting application requirements. AI techniques are feed with information provided by the on-line monitors and external sensors and are applied in a transparent way without compromising the safety of the system. To ensure safety requirements are preserved SELENE’s CCP relies on the strong isolation capabilities provided at hardware and software levels.
Fields of science
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- natural sciencescomputer and information sciencesartificial intelligence
- natural sciencescomputer and information sciencessoftware
- social sciencessociologyindustrial relationsautomation
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringsensors
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringrobotics
Programme(s)
Topic(s)
Funding Scheme
RIA - Research and Innovation actionCoordinator
46022 Valencia
Spain