Journal of Sensor Science and Technology (센서학회지)

The Korean Sensors Society (한국센서학회)
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Design and Implementation of Multi-mode Sensor Signal Processor on FPGA Device

다중모드 센서 신호 처리 프로세서의 FPGA 기반 설계 및 구현

  • Soongyu Kang (School of Electronics and Information Engineering, Korea Aerospace University) ;
  • Yunho Jung (Department of Smart Air Mobility, Korea Aerospace University)
  • 강순규 (한국항공대학교 항공전자정보공학과) ;
  • 정윤호 (한국항공대학교 스마트드론공학과)
  • Received : 2023.07.18
  • Accepted : 2023.07.30
  • Published : 2023.07.31
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Abstract

Internet of Things (IoT) systems process signals from various sensors using signal processing algorithms suitable for the signal characteristics. To analyze complex signals, these systems usually use signal processing algorithms in the frequency domain, such as fast Fourier transform (FFT), filtering, and short-time Fourier transform (STFT). In this study, we propose a multi-mode sensor signal processor (SSP) accelerator with an FFT-based hardware design. The FFT processor in the proposed SSP is designed with a radix-2 single-path delay feedback (R2SDF) pipeline architecture for high-speed operation. Moreover, based on this FFT processor, the proposed SSP can perform filtering and STFT operation. The proposed SSP is implemented on a field-programmable gate array (FPGA). By sharing the FFT processor for each algorithm, the required hardware resources are significantly reduced. The proposed SSP is implemented and verified on Xilinxh's Zynq Ultrascale+ MPSoC ZCU104 with 53,591 look-up tables (LUTs), 71,451 flip-flops (FFs), and 44 digital signal processors (DSPs). The FFT, filtering, and STFT algorithm implementations on the proposed SSP achieve 185x average acceleration.

Keywords

Acknowledgement

본 연구는 산업통상자원부의 기술혁신사업 (No.00144288, 00144290)의 일환으로 수행되었으며, CAD tool은 IDEC에 의해 지원되었음.

References

  1. Y. Perwej, M. A. AbouGhaly, B. Kerim, and H. A. M. Harb, "An Extended Review on Internet ot Things (IoT) and its Promising Applications", Commun. Appl. Electronics (CAE), ISSN, Vol. 7, No. 26, pp. 8-22, 2019. https://doi.org/10.5120/cae2019652812
  2. N. K. Suryadevara, and S. C. Mukhopadhyay, "Wireless Sensor Network Based Home Monitoring System for Wellness Determination of Elderly", IEEE Sens. J., Vol. 12, No. 6, pp. 1965-1972, 2012. https://doi.org/10.1109/JSEN.2011.2182341
  3. S. Kang, H. Kim, C. Park, Y. Sim, and Y. Jung, "sEMG-based Hand Gesture Recognition Using Binarized Neural Network", Sensors, Vol. 23, No. 3, pp. 1436-1451, 2023.
  4. M. Z. Arshad, D. Jung, M. Park, H. Shin, J. Kim, and K. Mun, "Gait-based Frailty Assessment using Image Representation of IMU Signals and Deep CNN", Proc. of 2021 43rd Annual Int. Conf. IEEE Eng. in Med. Biol. Soc. (EMBC), pp. 1874-1879, 2021.
  5. K. Abratkiewicz, P. Krysik, Z. Gajo, and P. Samczynski, "Target Doppler Rate Estimation Based on the Complex Phase of STFT in Passive Forward Scattering Radar", Sensors, Vol. 19, No. 16, pp. 3627-3639, 2019. https://doi.org/10.3390/s19163627
  6. J. Heo, Y. Jung, S. Lee, and Y. Jung, "FPGA Implementation of an Efficient FFT Processor for FMCW Radar Signal Processing", Sensors, Vol. 21, No. 19, pp. 6443-6458, 2021. https://doi.org/10.3390/s21196443
  7. N. N. Alajlan and D.M Ibrahim, "TinyML: Enabling of Inference Deep Learning Models on Ultra-Low-Power IoT Edge Devices for AI Applications", Micromachines, Vol. 13, No. 6, pp. 851-872, 2022. https://doi.org/10.3390/mi13060851
  8. V. Aivaliotis, K. Tsantikidow, and N. Sklavos, "IoT-Based Multi-Sensor Healthcare Architectures and a Lightweight-Based Privacy Scheme", Sensors, Vol. 22, No. 11, pp. 4269-4279, 2022. https://doi.org/10.3390/s22114269
  9. M. Wang, F. Wang, S. Wei, and Z. Li, "A pipelined area-efficient and high-speed reconfigurable processor for floating-point FFT/IFFT and DCT/IDCT computations", Microelectronics J., Vol. 47, pp. 19-30, 2016. https://doi.org/10.1016/j.mejo.2015.11.004
  10. S. Mach, D. Rossi, G. Tagliavini, A. Marongiu, and L. Benini, "A Transprecision Floating-point Architecture for Energy-Efficient Embedded Computing", Proc. of 2018 IEEE Int. Symp. Circuits Syst. (ISCAS), pp. 1-5, Florence, Italy, 2018.
  11. G. Tagliavini, S. Mach, D. Rossi, A. Marongiu, and L. Benini, "A Transprecision Floating-Point Platform for Ultra-Low Power Computing", Proc. of 2018 Design, Autom. Test in Eur. Conf. Exhib. (DATA), pp. 1051-1056, Dresden, Germany, 2018.
  12. https://www.xilinx.com/products/boards-and-kits/zcu104.html#overview (retrieved on June. 21, 2023).
  13. https://www.dfrobot.com/product-1661.html/(retrieved on June. 21, 2023).
  14. https://www.infineon.com/cms/en/product/evaluation-boards/demo-sense2gol/ (retrieved on June. 21, 2023).