PhD Studentship: Using Artificial Intelligence and Learning to Direct Stimuli Generation in Functional Verification: Intelligent, Agent-Based Testing (supported by Infineon Technologies UK Ltd)

University of Bristol - Computer Science

The project:  Functional verification ensures the correct functioning of complex semiconductors. Companies invest heavily in functional verification, e.g. to ensure a good user experience, to comply with regulators, to avoid the considerable costs of recalls and litigation and, in safety critical applications, to prevent the loss of life.

The two state-of-the-art approaches to functional verification are constrained-random verification and formal verification. The former is inefficient, with many simulation cycles spent exploring the same state space in much the same way; guiding the tool into the interesting corner cases present in complex systems typically requires considerable input from engineers. On the other hand, formal verification can find corner cases with little manual steering, but complexity limits mean that it can only be applied exhaustively to relatively small designs. 

We aim to use AI techniques and learning to direct the generation of stimuli so that the interesting corner cases on a large complex design can be reached in an automated way. In this project we will develop intelligent, agent-based test methods, exploiting multi-agent systems for model-based test generation. An agent-based approach offers high-level, goal directed planning. Augmented with learning, based on reward and punishment, agent-based test generation can be further automated. The effectiveness of this technique has already been demonstrated in the context of human-robot interaction, where the goal is to execute an (unknown) sequence of actions to achieve a desired task. We now aim to transfer our agent-based stimuli generation approach from robotics into semiconductor design verification. In particular we will investigate how this approach can be used to automatically generate effective instruction sequences for processor verification, e.g. to achieve an architecturally defined state in a difficult to reach situation. We expect that this technique significantly reduces the need for hand-written constraints and random generation.

How to apply:  Please make an online application for this project at Please select <Computer Science> on the Programme Choice page and enter details of the studentship when prompted in the Funding and Research Details sections of the form together with the name of the supervisor, Prof. Kerstin Eder.

The successful candidate must be able to start mid-September 2017.

Candidate requirements:  Open to UK/EU students

A minimum 2.1 honours degree or equivalent in Computer Science or Mathematics.

Basic skills and knowledge required:

Excellent programming skills and a good understanding of computer architecture are essential.

You are able to quickly pick up new programming languages and you are willing learn how to use state-of-the-art professional EDA tools. You are a competent presenter, writer and communicator.

You seek an intellectual challenge and aim to achieve excellence in your research.

Funding: UK/EU (EU applicants who have been resident in the UK for 3 years prior to application) PhD tuition fees and a tax-free stipend at the current RCUK rate (£14,296 in 2016/17), enhanced by an additional industrial top-up subject to contracts. EU nationals resident in the EU may also apply but will qualify only for PhD tuition fees

Contacts: Prof. Kerstin Eder (

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