Application-specific soft-core vector processor for advanced driver assistance systems

Implementing convolutional neural networks for scene labelling is a current hot topic in the field of advanced driver assistance systems. The massive computational demands under hard real-time and energy constraints can only be tackled using specialized architectures. Also, cost-effectiveness is an important factor when targeting lower quantities. In this PhD thesis, a vector processor architecture optimized for FPGA devices is proposed. Amongst other hardware mechanisms, a novel complex operand addressing mode and an intelligent DMA are used to increase perfromance. Also, a C-compiler support for creating applications is introduced.