Reservoir Computing with Connected Magnetic Nanoring Ensembles

The concept of in-materia computing uses the natural complexity of material systems to perform computational operations naturally as part of the system’s inherent response to input stimuli. However, to implement a material system for computation effectively, the physical response of the system must be understood and exploited under a suitable computational framework. This thesis explores the application of arrays of interconnected magnetic nanorings for computation under the framework of reservoir computing. By using a combination of experimental and simulation techniques, the work presented here aims to explore and understand the response of the nanoring arrays, exploit their interesting dynamic properties for computation, and expand upon the computational power achievable with the system. Firstly, the implementation of a phenomenological model of the nanoring arrays is described, then validated against a range of experimental data covering the static, dynamic, and microstate response of the nanoring arrays with good agreement. This model then serves as a testbed for establishing a suitable paradigm for computing with the nanorings and exploring the computational properties of different regimes of response, ending with a proof-of-concept demonstration of reservoir computing with the nanorings on a benchmark spoken digit recognition task. Next, the findings made in simulation are used to inform the development of an experimental demonstration of computation. This involved the creation of experimental apparatus to apply stimuli to the nanorings via rotating magnetic fields, and to measure the evolving anisotropic magnetoresistance of the device. Interesting dynamic properties of the system’s resistance response are identified and paired with specific reservoir architectures that leverages them to provide different computational properties, evidenced by state-of-the art performances in a range of standard tasks. Finally, the changes in physical behaviour due to manipulations of the array’s lattice structure are explored at the microstate level as well as their macroscale response. Computational properties of the different arrangements are evaluated, and lack of microstate resolution in the readout mechanism is attributed to the subtlety of the differences. However, the additional computational power available when different arrangements are combined shows promising scalability for devices of the nanoring arrays

Optimising network interactions through device agnostic models

Physically implemented neural networks hold the potential to achieve the performance of deep learning models by exploiting the innate physical properties of devices as computational tools. This exploration of physical processes for computation requires to also consider their intrinsic dynamics, which can serve as valuable resources to process information. However, existing computational methods are unable to extend the success of deep learning techniques to parameters influencing device dynamics, which often lack a precise mathematical description. In this work, we formulate a universal framework to optimise interactions with dynamic physical systems in a fully data-driven fashion. The framework adopts neural stochastic differential equations as differentiable digital twins, effectively capturing both deterministic and stochastic behaviours of devices. Employing differentiation through the trained models provides the essential mathematical estimates for optimizing a physical neural network, harnessing the intrinsic temporal computation abilities of its physical nodes. To accurately model real devices' behaviours, we formulated neural-SDE variants that can operate under a variety of experimental settings. Our work demonstrates the framework's applicability through simulations and physical implementations of interacting dynamic devices, while highlighting the importance of accurately capturing system stochasticity for the successful deployment of a physically defined neural network

A perspective on physical reservoir computing with nanomagnetic devices

Neural networks have revolutionized the area of artificial intelligence and introduced transformative applications to almost every scientific field and industry. However, this success comes at a great price; the energy requirements for training advanced models are unsustainable. One promising way to address this pressing issue is by developing low-energy neuromorphic hardware that directly supports the algorithm's requirements. The intrinsic non-volatility, non-linearity, and memory of spintronic devices make them appealing candidates for neuromorphic devices. Here we focus on the reservoir computing paradigm, a recurrent network with a simple training algorithm suitable for computation with spintronic devices since they can provide the properties of non-linearity and memory. We review technologies and methods for developing neuromorphic spintronic devices and conclude with critical open issues to address before such devices become widely used

Quantifying the computational capability of a nanomagnetic reservoir computing platform with emergent magnetisation dynamics

Devices based on arrays of interconnected magnetic nano-rings with emergent magnetization dynamics have recently been proposed for use in reservoir computing applications, but for them to be computationally useful it must be possible to optimise their dynamical responses. Here, we use a phenomenological model to demonstrate that such reservoirs can be optimised for classification tasks by tuning hyperparameters that control the scaling and input-rate of data into the system using rotating magnetic fields. We use task-independent metrics to assess the rings' computational capabilities at each set of these hyperparameters and show how these metrics correlate directly to performance in spoken and written digit recognition tasks. We then show that these metrics, and performance in tasks, can be further improved by expanding the reservoir's output to include multiple, concurrent measures of the ring arrays' magnetic states.ISSN:0957-4484ISSN:1361-652