From Stochasticity to Functionality: Probabilistic Computation with Magnetic Nanowires

Lead Research Organisation: University of Sheffield
Department Name: Materials Science and Engineering


Machine learning, or the ability of computers to intelligently analyse complex data sets, may be the defining technology of our age. Society produces huge amounts of data in areas as diverse as economics, weather, medicine, science and social media, analysis of which can greatly enhance our decision making. However, current computers are poorly suited to analysing large and complex datasets, particularly when compared to animal and human brains.

For example, while a typical contemporary computer can perform simple numerical calculations much more quickly than the brain, its efficiency is almost one million times lower when performing more complex data analysis such as recognising human faces. This inefficiency results from attempts to emulate "neuromorphic", or brain-like, computational processes by brute force on hardware which is ill-suited to the task. For example, in a conventional computer memory and processing are inherently separated, whereas they share the same medium in the brain. To overcome these limitations, bespoke computers that are specifically designed for neuromorphic computation are required.

Away from pure computational power, the "big data" revolution has been driven by our ability to store information. This is the result of nano-scale magnetic technology in the form of hard-disk drives. However, nanomagnetic devices suffer from an encroaching limitation, in that their behaviour becomes increasingly unreliable, or stochastic, as they are further miniaturised to increase performance. While this is devastating for conventional digital computers, there is strong evidence that stochastic behaviour can be tolerated in, or even enhance, the performance of neuromorphic technologies. This raises the tantalising prospect of nanomagnetic technology being ideally suited for developing new computer forms of computer hardware for data analysis.

In this project, a collaboration between experts in nanomagnetic technology and computer science at the University of Sheffield, we will perform a pilot study to investigate whether stochastic behaviour in "magnetic domain wall devices" a promising form of magnetic nanotechnology where magnetic information is flowed through nanowire conduits, can be used to realise new, neuromorphic computer architectures. Working with an advisory board of academic and industry experts in machine learning technology we will demonstrate new data analysis devices, before creating a roadmap to develop the devices into real technology. Eventual success in developing these technologies could result in powerful hardware platforms that can provide even the smallest device the ability to intelligently analyse its environment to make informed decisions.

Planned Impact

The aim of this project is to lay the foundation for new forms of neuromorphic computer hardware platforms that are specifically designed to natively implement machine learning approaches to data analysis. At this conceptual stage our aim is to demonstrate the feasibility of these devices, and to gain an understanding of how their ultimate performance might compare to other technologies that are currently under development. The primary impact of the work during the project's lifetime will therefore be the generation of new knowledge, and the major outputs of the project will be papers in academic journals and presentations at international conferences. Thus, in the short term (1-5 years) our research will impact the academic community through our research-enabling results, which we believe will inspire entirely new lines of enquiry.

By the end of our project we aim to have created a roadmap indicating how the most promising of our results can be moved forward towards commercial devices. Such developments will require a consortium approach and require us to engage a range of industry experts in areas such as machine learning and computational hardware architecture. Thus, beyond the end of our project (5-10 years) we aim to begin to generate industrial impact through collaborative research. To assist us in achieving this we have assembled a project advisory board that includes researchers from a major semiconductor and software company (ARM) and a successful spin out company working to develop neuromorphic computing technology (aiCTX). The expertise of our advisory board will be invaluable in steering our project towards directions with genuine technological promise and in developing industry contacts.

Developing technologies to intelligently analyse data is one of the defining challenges of our era. These technologies may take the form of small scale systems targeted at ubiquitous computing applications, or larger scale systems aimed at tackling "big data" problems in a fast and efficient manner, and will have a transformational impact on areas as diverse as economics, climate monitoring, security, safety, healthcare, science and social media. Therefore, looking further into the future (10+ years), our research has the potential to create substantial societal and economic impact through the enabling of new powerful technologies.

Success in our programme of research will require detailed knowledge of contemporary nanoscale fabrication and characterisation techniques, machine learning algorithms, and mathematical modelling techniques. The two post-doctoral researchers employed on this project will gain training in each of these, thus providing them with an extensive portfolio of skills that will leave well equipped for their future careers in academia or in the private sector. A good supply of scientists and engineers with such skills is key to the future success of the UK economy. The impact here will be realised over the 3-year timescale of the project.

The project team have strong track records of public engagement activities and frequently deliver outreach activities to primary/secondary school and A-level students. We will use our strong links with schools and colleges to deliver talks to students, and will seek to engage with the general public through creating short videos disseminated through online platforms, and by presenting at exhibitions the University of Sheffield's annual Festival of the Mind, the Big Bang Fair and the Cheltenham Science Fair. These outreach activities will benefit the general public over the lifespan of the project (1-3 years).


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Description The project is in its early stages, however we have already made significant steps in delivering its objectives:

(1) We have produced preliminary results demonstrating how the stochastic behaviours of magnetic nanowire devices can be used to realise the paradigm of stochastic computing, where floating point numbers are encoded within random bitstreams and simple logic gates perform complex logical operations. For example, we have shown how an external stimulus applied to a defect site can allow the tuning of domain wall pinning probabilities. This can be used for the generation of random bitstreams with tuneable average values. We have also shown how magnetic AND gates can be used to multiply the values encoded in random bitstreams. Progress is being made towards the realisation of further types of logic gates within this framework.

(2) We have produced numerical models, showing that nanowire-based stochastic synapses can be used to realise feed-forward neural networks (e.g. for written digit recognition) and restricted Boltzmann machines.

(3) We have used newly developed mathematical model to show how emergent, stochastic domain wall dynamics within magnetic ensembles can be used to perform a range machine learning tasks, including speech recognition.
Exploitation Route As yet our findings show how nanomagnetic devices can be used to realise a variety different computational paradigms using magnetic devices. These are not yet ready for industrial development, but we aim to have a roadmap of how this will occur by the end of the project. Machine learning is now ubiquitous in society, and new devices that can realise it in an efficient manner would likely have an influence on a huge range of areas including smart technolgies, prediction of complex systems such as the weather and financial markets, and security.
Sectors Aerospace, Defence and Marine,Communities and Social Services/Policy,Digital/Communication/Information Technologies (including Software),Electronics,Financial Services, and Management Consultancy,Manufacturing, including Industrial Biotechology,Security and Diplomacy

Description Collaboration with Dr Joe Friedman (University of Texas, Dallas) 
Organisation University of Texas
Country United States 
Sector Academic/University 
PI Contribution We have been investigating how magnetic nanowire devices can be natively used to realise data operations required to perform inference tasks.
Collaborator Contribution Dr Friedman has provided us support in understanding how nanomagnetic hardware could be used for creating devices that can perform inference tasks in hardware.
Impact So far the collaboration has not resulted in any outcomes, although we anticipate at least one paper and a grant application will result from it.
Start Year 2019