Exploring Hardware Security for Approximate Computing

Summary

The growing demand for low-power, high-performance computing systems has accelerated the development of a new paradigm called ‘Approximate Computing’, which often surpasses traditional architectures in efficiency. As a result, approximate computing is quickly being implemented in many practical applications such as image processing, AI and machine learning where exact results are not critical and intrinsic errors are acceptable. However, approximate computing also introduces security vulnerabilities mainly because the uncertain and unpredictable intrinsic errors during approximate execution may be indistinguishable from malicious modification of the input data, the execution process, and the results. Hackers may obtain critical information by targeting various components of an approximate computing system, such as software applications, processors, accelerators, memory units, and circuit designs. Through these attacks, they can gain unauthorized access to sensitive data. This highlights the need for effective security measures to protect these elements and ensure the integrity and confidentiality of the information processed within approximate computing systems.

Ensuring the integrity of data and processes within approximate computing requires innovative security measures that can safeguard against attacks without negating the energy and resource-saving benefits of approximate methods. In this PhD project, the candidate will explore new security strategies and designs specifically for approximate computing. By developing secure, resource-efficient approximate circuits and systems on FPGA, the research will focus on safeguarding neural network models and other computational frameworks against emerging threats.

During this project, the student will develop essential skills in hardware security focusing on designing secure approximate circuits and effective testing method. The student involved in this PhD project will first understand the working of open-source approximate systems available at hardware, software and algorithm levels. Then, the hardware security threats such as Hardware Trojans, Side Channel attacks, Firmware attacks etc will be investigated in context of these approximate systems. Finally, a methodology will be proposed to secure these approximate systems from security vulnerabilities. The low-power approximate neural networks (LPANNs) can be selected as a practical application where the student can propose a novel approach to secure LPANNs from existing hardware security threats. The student will gain hands-on experience with FPGA technology, focusing on creating secure circuits and architectures that safeguard data integrity in approximate systems. A background in computer engineering, electronics engineering, computer science, or a related field will be advantageous, along with knowledge of programming languages such as VHDL or Verilog and familiarity with machine learning frameworks relevant to secure computing.

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 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

Equal Opportunities

The University is an equal opportunities employer and welcomes applicants from all sections of the community, particularly from those with disabilities.

Appointment will be made on merit.

Funding and eligibility

This project is funded by:

• Department for the Economy (DfE)
• Vice Chancellor's Research Scholarship (VCRS)

Our fully funded PhD scholarships will cover tuition fees and provide a maintenance allowance of £19,237 (tbc) per annum for three years (subject to satisfactory academic performance).  A Research Training Support Grant (RTSG) of £900 per annum is also available.

These scholarships, funded via the Department for the Economy (DfE) and the Vice Chancellor’s Research Scholarships (VCRS), are open to applicants worldwide, regardless of residency or domicile.

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.