Lessons Learned from last 4 Years of Reconfigurable Computing

Abstract

Partial dynamic reconfiguration of FPGAs was investigated for video-based driver assistance applications during the last 4 years. High-level application software was combined with dynamically reconfigurable hardware accelerators in selected scenarios, e.g. vehicle lights detection, optical flow motion detection. From the beginning of the project, various research challenges have been targeted, including hardware/software partitioning between embedded RISC and accelerators, granularity of reconfigurable regions, as well as the impact of the reconfiguration process on system performance. This article will review the status of these research challenges and present an outlook on future challenges, including reconfiguration look ahead. Challenges will be illustrated on robotic vision scenarios with dynamically changing computational load from soft real-time and hard real-time applications.