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.
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. We have defined four key objectives, each of individual scientific merit that will contribute essential functionalities to the computing platform as a whole:
Objective 1—To establish protocols for reservoir computing based on nanomagnet ensemble behaviour, i.e. representing linear independent reservoir transfer functions (x_i (t)) with emergent ensemble properties and memory of past states through ensemble non-volatility.
Objective 2—To identify the most suitable approach for tuning interactions between nanomagnets, i.e. determine the effectiveness of different external stimuli in tuning inter-element coupling with optimized element geometry, arrangement, and materials.
Objective 3—To understand and demonstrate tuneable emergent behaviour in nanomagnet ensembles, i.e. determine the sensitivity of emergent properties of large nanomagnet ensembles to external stimuli as well as lattice geometries, material compositions, local inhomogeneities, and boundary effects.
Objective 4—To demonstrate computational functionality in nanomagnet ensembles. This will require demonstration of data read/write methods and integration of connections for read, write, and tuning operations on a large ensemble.
SpinENGINE brings together a multidisciplinary team of researchers with expertise in computer science, condensed matter physics, material science, computational modelling, and high-resolution microscopy. This 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 SpinENGINE project we have demonstrated that coupled nanomagnet ensembles and their emergent properties may serve as a promising platform for reservoir computing. We have developed simulator frameworks suitable for exploring such systems in simulation, methodologies for fabrication of physical implementations and promoted the use of task-independent metrics for evaluation of their performance as reservoirs. Finally, we have implemented demonstrator implementations, two lab-based and on palm sized implementation with all-electrical input and readout.
