Project description
Reservoirs of nanomagnets may boost processing power
Computing power struggles to keep up with analysis demands as data generation keep increasing exponentially. Realizing new platforms for massively parallel data processing, where large amounts of data are processed 'all at once' rather than piece-by-piece, is key to closing this gap. Now, SpinENGINE is combining two cutting-edge concepts, reservoir computing and nanomagnet ensemble dynamics, to realise this vision. Reservoir computing utilises a reservoir with highly nonlinear dynamics that projects input signals onto high-dimensional spaces and use simple linear processing techniques to extract an output. SpinENGINE is using the emergent and tuneable nonlinear interactions in nanomagnet ensembles as the reservoir to create a new massively parallel, computational device.
Objective
The SpinENGINE project will lay the foundations for a new, massively parallel, computational platform based on emergent behaviour in large nanomagnet ensembles. The project will develop an efficient, highly scalable, and easily reproducible platform meeting the data analysis challenges in our increasingly data-rich society. We will build upon our recent discoveries and use complex, nonlinear, and highly tunable interactions in such ensembles to realize a hardware platform for “Reservoir Computing”, a biologically-inspired computational approach. Our critical hypothesis is that the synergies between the inherent properties of nanomagnet ensembles and those required for reservoir computing will enable the efficient creation of a highly adaptive computational platform for the analysis of complex, dynamic data sets. This has the potential to greatly outperform current approaches using conventional CMOS hardware.
SpinENGINE will bring together a multidisciplinary team of researchers with expertise in computer science, condensed matter physics, material science, computational modelling, and high-resolution microscopy. This will enable us to simultaneously explore the fundamental behaviours of nanomagnet ensembles and understand how these can be harnessed for useful computation. By the end of the project, we aim to fabricate a proof-of-concept device capable of solving pattern recognition and classification problems, and, in collaboration with our industrial partner, IBM, produce a roadmap to the further scaling and commercialization of our computational platform. Success in the SpinENGINE project will have vast implications for data analysis at all scales, ranging from low power computation in the simplest sensor node to accelerated data processing in the most complex supercomputer.
Fields of science
- natural sciencesphysical sciencescondensed matter physics
- natural sciencesphysical sciencesopticsmicroscopy
- natural sciencescomputer and information sciencesartificial intelligencepattern recognition
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringcomputer hardwaresupercomputers
- natural sciencescomputer and information sciencesdata sciencedata processing
Keywords
Programme(s)
Funding Scheme
RIA - Research and Innovation actionCoordinator
7491 Trondheim
Norway