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IKTPLUSS-IKT og digital innovasjon

SOCRATES: Self-Organising Computational substRATES

Alternative title: Selv-organiserende materialer for informasjonsprosessering

Awarded: NOK 16.0 mill.

The growth of data will influence all information processing system, from the simplest sensor node to the most complex supercomputer. Contemporary computer systems struggle with energy efficiency and basic architectural to meet the challenge of today's vast data growth. SOCRATES is a long-term time horizon project seeking radical breakthroughs toward efficient and powerful data analysis available everywhere. SOCRATES will take inspiration from biology and neuroscience to exploit novel substrates that support self-organization through local interactions to create a theoretical and experimental foundation for a new computing paradigm. In the SOCRATES project exploit the self-organizing properties of living neural cultures as a method for creating new information processing hardware in nanomagnetc substrates. Living neural cultures of SOCRATES are grown to collect data on how such network organize. Data from current neural cultures have been collected and are analysed to detect network properties related to self-organization, i.e. network structure and behavior. Models of network behavior is used to investigate the computational abilities of these biological systems toward incorporation in our targeted nanomagnetic computing hardware. To achieve the goals of SOCRATES we need production, or cultivation for living neural networks, of our physical systems as well as analytic models and simulation tools. Recently the SOCRATES team has fabricated nanomagnet ensembles at NTNU NanoLab. Nanosystems consisting of up to approx 100 000 nanomagnets with several geometries (architectural layouts) have been produced. To be able to measure behavior we have used international laboratory with the needed synchrotron facilities (X-PEEM) and local laboratory facilities at NTNU (MFM). For our living neural networks human engineered neuronal subtypes and neurons derived from rodent brain has been used to studied emergent behavior, including the possibility to influence structural and/or functional dynamics. Simulation of the physical system is important toward a deeper understanding of the underlying complex systems. However, simulations are even more important to be able to propose parameters for the physical systems favorable for computing. In 2021 a version of our EvoDynamic simulation framework was made available as open source. EvoDynamic enable simulated behavior of different complex system architectures to gain insight in such systems when computation is targeted. For nanosystem simulation a simulator capable of abstracting the complex underlying physics of nanomagnet ensembles to a level where large systems, over a million nanomagnets, has been successfully simulated for the first time. In 2021 the model for temperature has been changed to support absolute temperature models to support basic research in physics. The simulator, flatspin, is a scalable simulator that can run on CPUs or exploit GPU-acceleration (from single GPU to GPU clusters). flatspin is available as open source. The flatspin simulator is now used by several groups internationally. New information processing hardware in nanomagnetc substrates is the main goal of the SOCRATES project. Combining knowledge from simulations and experimental works the project has made progress toward reservoir computation in our nanosystems (artificial spin ices). Geometries with promising computational properties (the pinwheel geometry) was tuned to act as a computational substrate that enabled good performance on benchmark problems. As such, we have shown that our new substrate can perform a set of necessary fundamental computational properties. A necessary step toward new computational substrates.

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. 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. In SOCRATES, a fundamentally new hardware approach based on principles from morphogenetic engineering (i.e., dynamic development of complex heterogeneous structures) will be developed to meet these requirements. SOCRATES will leverage substrates with self-organizing and emergent behaviour 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 behaviour in biological neural nets and ensembles of nanomagnets that have all the desired properties of an ideal system for data analysis.

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IKTPLUSS-IKT og digital innovasjon