Topic Introduction

In application domains like image processing or data analysis, ever-increasing performance demands push the capabilities of computational systems to their limits. This is especially true for professional imaging systems such as motion picture cameras that have to handle growing resolution, frame-rate and dynamic range. With technology scaling plateauing out, engineers are forced to rethink their approach to system design. The research field of approximate computing provides a new design paradigm which trades off accuracy against computational resources. The main challenge when applying this idea to real problems is the characterization and optimization of benefits while ensuring that quality levels do not fall short of certain acceptability standards.

Research Project

My PhD research is supervised by Prof. Dr.-Ing. Walter Stechele at the Chair of Integrated Systems as part of a research project on approximate computing for FPGA-based image processing in a cooperation of ARRI, Smartray & TUM funded by StMWi Bayern (IuK program).

This research project focusses on the application and optimization of approximation methods in professional image processing systems such as digital motion picture cameras (ARRI) and laser scanners (SmartRay) which are based on FPGA devices. We intend to use a SoC-based prototying system to characterize suitable approximation methods and we plan to utilize machine learning methods (like rule-based systems) to optimize approximation parameters.