|Funding for:||EU Students|
|Placed On:||17th April 2019|
|Closes:||31st July 2019|
Funding amount Minimum £15,009 p.a. for 4 years subject to confirmation and eligibility status plus an industrial top-up of at least £6,000 p.a.
We will investigate how existing and novel ML techniques can be used to improve the results of simulation-based hardware design verification. Areas of improvement may include design coverage, bug discovery probability, the number of simulations required to reach verification goals, the novelty of the internal states the design experiences during simulation, the novelty of the simulation’s outputs, or other areas we may choose. We will prefer techniques that require no human intervention, which are as easy as possible to train, and which can continue to learn from large numbers of data points. To enable this, Arm will provide access to problems and datasets of both academic and industrial interest, as well as collaboration with industrial researchers.
We are offering an opportunity to advance the state of ML in an area that is both highly valuable to industry, and not already saturated with competing ML researchers. The hardware verification process naturally creates large amounts of labeled data in a reproducible way, enabling research on novel datasets without requiring manual data gathering and annotation. However, the data in this domain is a challenge because hardware designs often do not have a continuous mapping between input stimuli and output behavior. We are interested both in practical results (e.g. improved bug discovery) and in theoretical results (e.g. new ML algorithms).
How to apply: Please make an online application at [http://www.bris.ac.uk/pg-howtoapply] as soon as possible.
In the Funding section, please state “I would like to be considered for an iCASE award from the Computer Science Department.” and specify the title of the scholarship.
A good 2:1 or first-class degree in Computer Science, Computer Systems Engineering, Informatics, Microelectronic Design or a similar discipline.
Excellent programming skills. A good understanding of computer architecture and/or machine learning techniques.
A background in at least one of the following areas: machine learning, processor architecture and design, simulation-based testing, model-based design or testing techniques. A competent presenter, writer and communicator, willing and able to work with our industrial collaborator.
The scholarship covers full UK/EU (EU applicants who have been resident in the UK for 3 years prior to 1 September 2019) PhD tuition fees and a tax-free stipend at the current RCUK rate (£15,009 in 2019/20). EU nationals resident in the EU may also apply, but will only qualify for PhD tuition fees.
Informal enquiries please contact Prof Kerstin Eder - Kerstin.Eder@bristol.ac.uk
General enquiries please contact email@example.com
Type / Role: