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Investigating execution-characteristics of feature-detection algorithms
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
Ericsson AB, Stockholm, Sweden.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-1687-930X
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0001-7586-0409
2018 (English)In: IEEE Conference on Emerging Technologies and Factory Automation, ISSN 1946-0740, E-ISSN 1946-0759, Vol. Part F134116, p. 1-4Article in journal (Refereed) Published
Abstract [en]

We discuss how to obtain information of execution characteristics, such as parallelizability and memory utilization, with the final aim to improve the performance and predictability of feature and corner detection algorithms for use in e.g. robotics and autonomous machines. Our aim is to obtain a better understanding of how computer vision algorithms use hardware resources and how to improve the time predictability and execution time of such algorithms when executing on multi-core CPUs. We evaluate a fork-join model applicable to feature detection algorithms and present a method for measuring how well the algorithm performance correlates with hardware resource usage. We have applied our method to the Featured from Accelerated Segment Test (FAST) algorithm. Our characterization of FAST reveals that it is an algorithm with excellent parallelism opportunities, resulting in an almost linear speed-up per core. Our measurements also reveal that the performance of FAST correlates very little with the number number of misses in the L1 data cache, L1 instruction cache, data translation lookaside buffer and L2 cache. Thus, the FAST algorithm will not have a negative effect on the execution time when the input data fits in the L2 cache. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2018. Vol. Part F134116, p. 1-4
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:mdh:diva-38918DOI: 10.1109/ETFA.2017.8247758ISI: 000427812000193Scopus ID: 2-s2.0-85044481799OAI: oai:DiVA.org:mdh-38918DiVA, id: diva2:1195539
Conference
22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Limassol, CYPRUS, SEP 12-15, 2017
Available from: 2018-04-05 Created: 2018-04-05 Last updated: 2018-04-05Bibliographically approved

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Danielsson, JakobBehnam, MorisSjödin, Mikael

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