Evaluation of Computer Vision Algorithms Optimized for Embedded GPU:s.
Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Utvärdering av bildbehandlingsalgoritmer optimerade för inbyggda GPU:er (Swedish)
The interest of using GPU:s as general processing units for heavy computations (GPGPU) has increased in the last couple of years. Manufacturers such as Nvidia and AMD make GPU:s powerful enough to outrun CPU:s in one order of magnitude, for suitable algorithms. For embedded systems, GPU:s are not as popular yet. The embedded GPU:s available on the market have often not been able to justify hardware changes from the current systems (CPU:s and FPGA:s) to systems using embedded GPU:s. They have been too hard to get, too energy consuming and not suitable for some algorithms. At SICK IVP, advanced computer vision algorithms run on FPGA:s. This master thesis optimizes two such algorithms for embedded GPU:s and evaluates the result. It also evaluates the status of the embedded GPU:s on the market today. The results indicates that embedded GPU:s perform well enough to run the evaluatedd algorithms as fast as needed. The implementations are also easy to understand compared to implementations for FPGA:s which are competing hardware.
Place, publisher, year, edition, pages
2014. , 58 p.
Embedded GPU, Computer vision, CUDA
IdentifiersURN: urn:nbn:se:liu:diva-112575ISRN: LiTH-ISY-EX--14/4816--SEOAI: oai:DiVA.org:liu-112575DiVA: diva2:768419
Subject / course
Computer Vision Laboratory