Exit Menu

Fast implementation of Deep Neural Networks for IoT devices

Deep neural networks (DNNs) have found great success in many computer vision-related tasks. However, most of the existing DNNs require high computation and storage cost. In this project, the student will investigate how to implement DNN efficiently on low-powered IoT devices. The focus is on DNN model size reduction, fast implementation and efficient communication of DNN feature vectors under power and bandwidth constraints. The main focus is on vision-based tasks. But it could be used for other DNN related applications as well.

References: A Survey of Model Compression and Acceleration for Deep Neural Networks

How to apply

If you are interested in applying for the above PhD topic please follow the steps below:

  1. Contact the supervisor by email or phone to discuss your interest and find out if you woold be suitable. Supervisor details can be found on this topic page. The supervisor will guide you in developing the topic-specific research proposal, which will form part of your application.
  2. Click on the 'Apply here' button on this page and you will be taken to the relevant PhD course page, where you can apply using an online application.
  3. Complete the online application indicating your selected supervisor and include the research proposal for the topic you have selected.

Good luck!

This is a self funded topic

Brunel offers a number of funding options to research students that help cover the cost of their tuition fees, contribute to living expenses or both. See more information here: https://www.brunel.ac.uk/research/Research-degrees/Research-degree-funding. The UK Government is also offering Doctoral Student Loans for eligible students, and there is some funding available through the Research Councils. Many of our international students benefit from funding provided by their governments or employers. Brunel alumni enjoy tuition fee discounts of 15%.