Visible to the public Self-Adaptive FPGA-Based Image Processing Filters Using Approximate Arithmetics

TitleSelf-Adaptive FPGA-Based Image Processing Filters Using Approximate Arithmetics
Publication TypeConference Paper
Year of Publication2017
AuthorsPirkl, Jutta, Becher, Andreas, Echavarria, Jorge, Teich, Jürgen, Wildermann, Stefan
Conference NameProceedings of the 20th International Workshop on Software and Compilers for Embedded Systems
PublisherACM
Conference LocationNew York, NY, USA
ISBN Number978-1-4503-5039-6
Keywordsadaptive filtering, approximate computing, dynamic partial reconfiguration, FPGA, image processing, Metrics, pubcrawl, resilience, Resiliency, Scalability, SoC
Abstract

Approximate Computing aims at trading off computational accuracy against improvements regarding performance, resource utilization and power consumption by making use of the capability of many applications to tolerate a certain loss of quality. A key issue is the dependency of the impact of approximation on the input data as well as user preferences and environmental conditions. In this context, we therefore investigate the concept of self-adaptive image processing that is able to autonomously adapt 2D-convolution filter operators of different accuracy degrees by means of partial reconfiguration on Field-Programmable-Gate-Arrays (FPGAs). Experimental evaluation shows that the dynamic system is able to better exploit a given error tolerance than any static approximation technique due to its responsiveness to changes in input data. Additionally, it provides a user control knob to select the desired output quality via the metric threshold at runtime.

URLhttps://dl.acm.org/citation.cfm?doid=3078659.3078669
DOI10.1145/3078659.3078669
Citation Keypirkl_self-adaptive_2017