Deep Convolutional Neural Networks (ConvNets) have demonstrated state-of-the-art performance in many machine learning problems involving image classification and speech recognition. Over the last few years several advances in the design of ConvNets have not only led to a further boost in achieved accuracy on image recognition tasks but also played a crucial role as a feature extractor for other tasks such as object detection, localization, semantic segmentation and image retrieval. However, the complexity and size of ConvNets have limited their use in mobile applications and embedded systems.

This PhD studentship will investigate ways to optimize these deep neural networks using model-architecture co-design and enable mass deployment of deep-learning based applications in consumer products.

The PhD student will design architectures for hardware convolution engines for scenarios with limited hardware resources and tight power and latency constraints. The student will also investigate automated tools to solve the difficult problem of designing neural networks under complexity constraints and will describe a design-space-exploration tool that automatically discovers good neural network models with efficient hardware implementations.

Contact: Professor Klaus McDonald-Maier (kdm@essex.ac.uk), Dr Xiaojun Zhai (xzhai@essex.ac.uk), and Dr Shoaib Ehsan (sehsan@essex.ac.uk)

Closing date: 11 January 2019.