Optimization of artificial intelligence algorithms for deployment in embedded systems with application to autonomous driving

AI algorithms based on large neural networks currently provide state-of-the-art performance in several computer vision tasks, including real-time object recognition and semantic segmentation. Such recent advances in deep learning motivate the use of deep learning in sensing applications such as autonomous driving. However, the excessive computational and energy consumption requirements remain an important impediment for the deployment on constrained embedded devices. A recently explored solution space lies in compressing - approximating or simplifying - deep neural networks in some manner before use on the device. In the first part of this project, we will explore general-purpose techniques for compressing any type of very large and deep neural network - including fully-connected, convolutional, recurrent neural networks, as well as their combinations, and obtaining a global view of parameter redundancies. A number of methodologies will be explored, including reinforcement learning. In the second part of the project, in collaboration with Jaguar Land-Rover (JLR), we will further optimise the algorithms for a range of
applications in autonomous driving. The project falls within the remit of the EPSRC, which strongly recognises AI research's importance to data science, robotics and autonomous systems. This is a new venture between Montana's Data Science group and JLR's newly established group in Automated Driving headed by Joerg Schlinkheider.