Detail publikace

Performance comparison of a signal processing pipeline execution using CPU and GPU

TOMAŠOV, A. HORVÁTH, T.

Originální název

Performance comparison of a signal processing pipeline execution using CPU and GPU

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

The paper compares the execution performance of NumPy and PyTorch mathematical libraries in embedded systems with graphics processing unit (GPU) acceleration. Both frameworks execute a signal processing pipeline from a fiber manipulation detection system, which inspects a signal from a state of polarization analyzer to enhance the security of optical fiber. The performance comparison is evaluated in the NVIDIA Jetson Nano system with 128-core Maxwell GPU. Based on the measured results, the PyTorch library executed on the GPU has performance improvement from 59 % to 84 % on different batch sizes. The results prove the real-time analysis capabilities of such a system with GPU acceleration.

Klíčová slova

Fiber Optics Security, GPU, NVIDIA Jetson Nano, PyTorch, Signal Processing Acceleration

Autoři

TOMAŠOV, A.; HORVÁTH, T.

Vydáno

26. 4. 2022

Nakladatel

Brno University of Technology, Faculty of Electrical Engineering and Communication

Místo

Brno

ISBN

978-80-214-6029-4

Kniha

Proceedings I of the 28th Conference STUDENT EEICT 2022 General Papers

Edice

1

Strany od

465

Strany do

469

Strany počet

5

URL

https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2022_sbornik_1_v2.pdf

BibTex

@inproceedings{BUT177692,
  author="Adrián {Tomašov} and Tomáš {Horváth}",
  title="Performance comparison of a signal processing pipeline execution using CPU and GPU",
  booktitle="Proceedings I of the 28th Conference STUDENT EEICT 2022 General Papers",
  year="2022",
  series="1",
  pages="465--469",
  publisher="Brno University of Technology, Faculty of Electrical Engineering and Communication",
  address="Brno",
  isbn="978-80-214-6029-4",
  url="https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2022_sbornik_1_v2.pdf"
}