Hyperdimensional representations for neuromorphic computation

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Summary

Brainlike cognitive models that depend on very high dimensionality and randomness have emerged over the years. They represent things in high-dimensional vectors that are manipulated by operations that produce new high-dimensional vectors, in what is often referred to as hyperdimensional (HD) computing. These models consider the brain’s circuits to be represented by mathematical properties and envisage dealing with the hypothesised HD representation and arithmetic through computation of vectors, matrices, permutations, and probability. HD computing provides a natural fit to implement models on non-von Neumann architectures (a.k.a. in-memory computing) using emerging technologies. HD computing has been applied in areas such as recognition, information retrieval and sensorimotor control.

Humans naturally integrate multiple senses to cognitively process information and make decisions to complete actions. For example, dexterous actions involve decision making and synchronisation of hands and fingers using senses such as vision and touch. Neuromorphic sensors offer autonomous systems the ability to sense in a manner similar to eyes, ears or fingers with low power consumption. Neuromorphic sensors provide the ability to represent data in a brain-like manner. However, neuromorphic systems still lack specialised brainlike cognitive models in which they can be interfaced and used to efficiently perceive, process and act on information.

HD computing offers us the best opportunity to demonstrate brainlike performance using neuromorphic sensors. HD computing can permit integration of multisensory inputs and generation of action outputs in a transparent, explainable way, overcoming existing problems in AI around generalisation, explainability, power consumption and excessive carbon generation. This project will focus on the application of hyperdimensional computing for neuromorphic applications.

Please note that a research proposal is NOT required for this project.

Essential criteria

Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.

We may also consider applications from those who hold equivalent qualifications, for example, a Lower Second Class Honours Degree plus a Master’s Degree with Distinction.

In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications.

• Experience using research methods or other approaches relevant to the subject domain
• A comprehensive and articulate personal statement
• A demonstrable interest in the research area associated with the studentship

Desirable Criteria

If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.

• First Class Honours (1st) Degree
• Masters at 70%
• For VCRS Awards, Masters at 75%
• Experience using research methods or other approaches relevant to the subject domain
• Work experience relevant to the proposed project
• Publications - peer-reviewed
• Experience of presentation of research findings

Funding and eligibility

The University offers the following levels of support:

Vice Chancellors Research Studentship (VCRS)

The following scholarship options are available to applicants worldwide:

• Full Award: (full-time tuition fees + £19,000 (tbc))
• Part Award: (full-time tuition fees + £9,500)
• Fees Only Award: (full-time tuition fees)

These scholarships will cover full-time PhD tuition fees for three years (subject to satisfactory academic performance) and will provide a £900 per annum research training support grant (RTSG) to help support the PhD researcher.

Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Please note: you will automatically be entered into the competition for the Full Award, unless you state otherwise in your application.

Department for the Economy (DFE)

The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £19,000 (tbc) per annum for three years (subject to satisfactory academic performance).

This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.

• Candidates with pre-settled or settled status under the EU Settlement Scheme, who also satisfy a three year residency requirement in the UK prior to the start of the course for which a Studentship is held MAY receive a Studentship covering fees and maintenance.
• Republic of Ireland (ROI) nationals who satisfy three years’ residency in the UK prior to the start of the course MAY receive a Studentship covering fees and maintenance (ROI nationals don’t need to have pre-settled or settled status under the EU Settlement Scheme to qualify).
• Other non-ROI EU applicants are ‘International’ are not eligible for this source of funding.
• Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Due consideration should be given to financing your studies. Further information on cost of living

Recommended reading

[1] Kleyko, Denis, et al. "A survey on hyperdimensional computing aka vector symbolic architectures, part ii: Applications, cognitive models, and challenges." ACM Computing Surveys 55.9 (2023): 1-52.

[2] Karunaratne, Geethan, et al. "In-memory hyperdimensional computing." Nature Electronics 3.6 (2020): 327-337.

[3] Mitrokhin, Anton, et al. "Learning sensorimotor control with neuromorphic sensors: Toward hyperdimensional active perception." Science Robotics 4.30 (2019).

The Doctoral College at Ulster University