Optimize Image Processing Algorithm on ARM Cortex-A72 and A53
DOI:
https://doi.org/10.26555/jiteki.v8i3.24457Keywords:
Multi-core Processing, Real-time System, Computer Vision, Image Processing, Image Feature Extraction, NanoPi M4V2, Embedded SystemAbstract
This work presents a technique to optimize processing image algorithms. The increasing demand for video applications like context-aware computing on mobile embedded systems requires the use of computationally intensive image processing algorithms. The system engineer has the mandate to optimize them so as to meet real-time deadlines. A methodology to take advantage of the asymmetric dual-core processor, which includes an ARM supported by shared memory, is presented with implementation details. The target platform chosen is the NanoPi M4V2. It has a dual-core and quad-core architecture with an ARM Cortex-A72 and Cortex-A53. The basic image correlation algorithm is chosen for benchmarking as it finds widespread application for various template-matching tasks such as face recognition. The basic algorithm prototypes conform to OpenCV, a popular computer vision library. OpenCV algorithms can be easily ported to the ARM core, which runs a popular operating system such as Linux. The algorithms are tested on a variety of images, and performance results are presented, measuring the speedup obtained due to dual-core and quad-core implementation. A major advantage of this approach is that it allows the ARM processor to perform important real-time tasks.Downloads
Published
2022-10-13
How to Cite
[1]
R. Muwardi, M. Yunita, H. U. Ghifarsyam, and H. Juliyanto, “Optimize Image Processing Algorithm on ARM Cortex-A72 and A53”, J. Ilm. Tek. Elektro Komput. Dan Inform, vol. 8, no. 3, pp. 399–409, Oct. 2022.
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