NFR project

SOCRATES

SOCRATES is a long-term time horizon project seeking radical breakthroughs toward efficient and powerful data analysis available everywhere, from the simplest sensor node to the most complex supercomputer.

About

SOCRATES (Self-Organizing Computational substRATES) is a long-term time horizon project seeking radical breakthroughs toward efficient and powerful data analysis available everywhere, from the simplest sensor node to the most complex supercomputer.

SOCRATES will exploit novel substrates that support self-organization through local interactions to create a theoretical and experimental foundation for a new computing paradigm. Such a complex systems approach to analytics opens for a radical breakthrough in the field of computing, alleviating main problems of contemporary computer systems relating to energy efficiency, scalability, and self-learning.

The data analytic challenge is importunate in today's increasingly data-rich society. Where a staggering 2.5 exabytes are created every day and emerging technologies like the Internet of things (IoT) will substantially increase the data growth rate, and further increase the demand for efficient analysis. To achieve efficient analysis everywhere, fundamentally new hardware approaches that are efficient, scalable, and may be adapted to the needs of diverse and complex data analysis tasks are required. An ideal system for realization of efficient hardware should be capable of vast parallel processing of data with inherent parallel learning capability.

SOCRATES will leverage substrates with self-organizing and emergent behavior to create systems with the property of inherently changing state transition functions and the set of state variables over time (caused by bio-inspired morphological processes). We aim at creating a theoretical and experimental foundation of morphogenetic systems based on self-organizing and emergent behavior in biological neural nets and ensembles of nanomagnets that have all the desired properties of an ideal system for data analysis.