Design of a Configurable, Massive-Parallel Vector Processor Architecture for Computer Vision and a Framework for the Implementation of Object Recognition Applications for Embedded Systems

The increasing complexity of current computer vision algorithms for autonomous driving, such as object detection and classification using neural networks, represents a challenge for automotive system designers. Providing a real-time processing system under hard real-time constraints and a low energy (budget a few watts) is difficult to achieve even with current technical platforms. The goal of this project is to design a new approach of application-specific vector processor for FPGA implementation. The well-known overhead of other platforms (e.g. GPUs) shall be avoided by using several strategies: Novel functional mechanisms, a modular and customizable architecture and a suitable development framework, which is especially designed for the implementation of automotive applications.). An FPGA-based prototype will demonstrate the performance of the vector processor concept for a selected application at the end of the project.