Science

OVERVIEW APPROXIMATE COMPUTING STUDENT PROJECTS

Approximate Computing
for Motion Picture Camera Processing

Trading off resources (energy, area, performance)
against application quality in a controlled way

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.

Mobirise

Research questions

How to approximate?
- Which approximation methods are suitable and benefitial for image processing on FPGA?
- How can we model their benefits and errors accurately and efficiently?

Where to approximate?
- How can we find an optimal configuration of approximation parameters?
- Which quality-energy trade-off knobs are useable in FPGA design flows?

What is the target quality (bound)?
- Which quality metrics are suitable for the application and useable in the system optimization?
- Which threshold can be tolerated?

Publications


2023

S. Conrady, Approximate Computing for Motion Picture Camera Processing, Dissertation, TUM School of Computation, Information and Technology
(Link)

2022

M. Manuel, B. Hien, A. Kreddig, S. Conrady, A. Kreddig, N. Anh Vu Doan, W. Stechele, Region of Interest Based Non-dominated Sorting Genetic Algorithm-II: An Invite and Conquer Approach, Genetic and Evolutionary Computation Conference (GECCO '22)
(Link)

2021

S. Conrady, A. Kreddig, M. Manuel, N. Anh Vu Doan, W. Stechele, Model-Based Design Space Exploration for FPGA-based Image Processing Applications Employing Parameterizable Approximations, Microprocessors and Microsystems, Volume 87, 2021, 104386
(Link)

M. Manuel, A. Kreddig, S. Conrady, N. Anh Vu Doan, W. Stechele, Region of Interest-Based Parameter Optimization for Approximate Image Processing on FPGAs, International Journal of Networking and Computing, 2021, Volume 11, Issue 2, Pages 438-462
(Link)

A. Kreddig, S. Conrady, M. Manuel and W. Stechele, A Framework for Hardware-Accelerated Design Space Exploration for Approximate Computing on FPGA, 2021 24th Euromicro Conference on Digital System Design (DSD), 2021, pp. 1-8
(Link)

2020

N. A. Vu Doan, M. Manuel, S. Conrady, A. Kreddig, W. Stechele, Parameter Optimization of Approximate Image Processing Algorithms in FPGAs, 2020 Eighth International Symposium on Computing and Networking Workshops (CANDARW), 2020, pp. 74-80
(Link)

M. Manuel, A. Kreddig, S. Conrady, N. Anh Vu Doan and W. Stechele, Model-Based Design Space Exploration for Approximate Image Processing on FPGA, 2020 IEEE Nordic Circuits and Systems Conference (NorCAS), 2020, pp. 1-7
(Link)

2019

S. Conrady, M. Manuel, A. Kreddig, and W. Stechele. 2019. LCS-based automatic configuration of approximate computing parameters for FPGA system designs. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO '19)
(Link)

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