Impact of algorithmic approximation on quality-of-control for image-based control systems

作     者：Konstantinos Bimpisidis 

作者单位：Eindhoven University of Technology 

学位级别：硕士

导师姓名：Dip Goswami;Sajid Mohamed;Sayandip De

授予年度：2019年

摘      要：Image Processing (IP) applications have become popular with the advent of efficient algorithms and low-cost CMOS cameras with high resolution. However, IP applications are compute-intensive, consume a lot of energy and have long processing times. Image approximation has been proposed by recent works for an energy-efficient design of these applications. It also reduces the impact of long processing times. The challenge here is that the IP applications often work as a part of bigger closed-loop control systems, e. g. advanced driver assistance system (ADAS). The impact of image approximations that tolerate certain error on these image-based control (IBC) systems is very important. However, there is a lack of tool support to evaluate the performance of such closed-loop IBC systems when the IP is approximated. In this work, we study the impact of algorithmic approximation on the quality-of-control for IBC systems. We propose a framework for performance evaluation of image approximation on a closed-loop automotive IBC system. Our framework is written in C++ and uses V-REP as the simulation environment. For the simulation, V-REP runs as a server and the C++ module as a client in synchronous mode. We show the effectiveness of our framework using a vision-based lateral control example. Our results show that approximate computing allows to improve the processing time up to a factor of 3.5. The measurements on our framework allowed us to develop a thorough understanding on the impact of approximation and achieve an overall quality-of-control improvement of up to 50%, when using approximate computing.