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A Heterogeneous Multi-Core Based Biomedical Application Processing System and Programming Toolkit

  • Tassadaq Hussain
  • Amna Haider
  • Abdelmalik Taleb-Ahmed
Article
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Abstract

Due to the growth of biological databases and biomedical instruments, the high performance active (real-time) signal processing becomes a challenge for medical scientists and engineers. The medical applications require a high-performance signal processor which can process the scientific and engineering biomedical applications and is easy to program. In this article, we have suggested a biomedical sensor interface and heterogeneous multi-core processing architecture based biomedical application processing system (BAPS) and biomedical applications toolkit. The biomedical sensor interface supports multiple regular and complex medical signals and provides digital data to the processing system. The BAPS uses heterogeneous multi-core architecture that processes biomedical applications with the performance up to 10 billion operations per sec and accuracy of 1 μ sec. The biomedical application toolkit provides programmability by giving support of hardware-level, scientific and artificial intelligence programming. The BAPS provides a single embedded platform solution to process a wide range of biomedical signal and image processing applications. To prove the importance of the proposed system, we developed the BAPS hardware architecture and tested it with different biomedical applications. When compared the results of BAPS with the baseline system, the results show that BAPS improves active (real-time) applications performance up to 12.8 times and processes passive (non-real-time) application 7.4 times faster and improves the 4.84-time performance of artificial intelligence application. While comparing the power and energy, the BAPS draws 1.56 times less dynamic power and consumes 21.85 times less energy.

Keywords

FPGA Multi-core Embedded system HPC Parallel programming Biomedical 

Notes

Acknowledgments

The research leading to these results has received funding from the Higher Education Commission Pakistan under NRPU 2017-18. The authors would like to thank Unal Color of Education Research and Development (UCERD) Private Limited Islamabad for the support.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Tassadaq Hussain
    • 1
    • 2
  • Amna Haider
    • 1
    • 2
  • Abdelmalik Taleb-Ahmed
    • 3
    • 4
  1. 1.Riphah International UniversityIslamabadPakistan
  2. 2.UCERDIslamabadPakistan
  3. 3.Laboratory of Industrial and Human AutomationMechanics and Computer ScienceFamarsFrance
  4. 4.Université de Valenciennes et du Hainaut Cambrésis Bat MalvacheFamarsFrance

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