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

authored by

Stephan Nolting, Florian Giesemann, Julian Hartig, Achim Schmider, Guillermo Paya-Vaya

Abstract

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.

Organisation(s)

Institute of Microelectronic Systems

Type

Conference contribution

Publication date

2017

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Computer Networks and Communications, Computer Science Applications, Hardware and Architecture, Software

Electronic version(s)

https://doi.org/10.23919/FPL.2017.8056836 (Access: Closed)