IEMEK Journal of Embedded Systems and Applications (대한임베디드공학회논문지)

Institute of Embedded Engineering of Korea (대한임베디드공학회)
권호 표지
DOI QR코드

DOI QR Code

Collaborative Streamlined On-Chip Software Architecture on Heterogenous Multi-Cores for Low-Power Reactive Control in Automotive Embedded Processors

차량용 임베디드 프로세서에서 저전력 반응적 제어를 위한 이기종 멀티코어 협력적 스트리밍 온-칩 소프트웨어 구조

  • Received : 2022.10.14
  • Accepted : 2022.11.15
  • Published : 2022.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

This paper proposes a multi-core cooperative computing structure considering the heterogeneous features of automotive embedded on-chip software. The automotive embedded software has the heterogeneous execution flow properties for various hardware drives. Software developed with a homogeneous execution flow without considering these properties will incur inefficient overhead due to core latency and load. The proposed method was evaluated on an target board on which a automotive MCU (micro-controller unit) with built-in multi-cores was mounted. We demonstrate an overhead reduction when software including common embedded system tasks, such as ADC sampling, DSP operations, and communication interfaces, are implemented in a heterogeneous execution flow. When we used the proposed method, embedded software was able to take advantage of idle states that occur between heterogeneous tasks to make efficient use of the resources on the board. As a result of the experiments, the power consumption of the board decreased by 42.11% compared to the baseline. Furthermore, the time required to process the same amount of sampling data was reduced by 27.09%. Experimental results validate the efficiency of the proposed multi-core cooperative heterogeneous embedded software execution technique.

Keywords

Acknowledgement

본 논문은 교육부의 재원으로 한국연구재단 (NRF-2018R1A6A1A03025109, NRF-2022R1I1A3069260)의 지원을 받아 수행된 연구임. 본 논문은 과학기술정보통신부의 재원으로 정보통신기획평가원 (No. 2021-0-00944, No. 2022-0-00816, No. 2022-0-01170)의 지원을 받아 수행된 연구임.

References

  1. S. Elashri, A. Azim, "An Energy-aware Optimization Model for Real-time Systems Analysis and Design: Work-in-progress," In Proceedings of the 2021 International Conference on Embedded Software (EMSOFT '21), pp. 45-46, 2021.
  2. J. Chang, S. Oh, D. Park, "Accuracy-Area Efficient Online Fault Detection for Robust Neural Network Software-Embedded Microcontrollers," In Proceedings of the 2022 International Conference on Embedded Software (EMSOFT '22), 2022.
  3. H. R. Faragardi, B. Lisper, K. Sandstrom, T. Nolte, "A Resource Efficient Framework to run Automotive Embedded Software on Multi-core ECUs," Journal of Systems and Software, Vol. 139, pp. 64-83, 2018. https://doi.org/10.1016/j.jss.2018.01.040
  4. M. Uelschen, M. Schaarschmidt, C. Fuhrmann, C. Westerkamp, "Work-in-Progress: PowerMonitor: Design Pattern for Modelling Energy-Aware Embedded Systems," 2019 International Conference on Embedded Software (EMSOFT), pp. 1-2, 2019.
  5. S. H. Aldaajeh, S. Harous, S. Alrabaee, "Fault-Detection Tactics for Optimized Embedded Systems Efficiency," IEEE Access, Vol. 9, pp. 91328-91340, 2021. https://doi.org/10.1109/ACCESS.2021.3091617
  6. N. Navet, F. Simonot-Lion, Automotive Embedded Systems Handbook, CRC Press, 2017.
  7. M. Ashjaei, L. L. Bello, M. Daneshtalab, G. Patti, S. Saponara, S. Mubeen, "Time-Sensitive Networking in Automotive Embedded Systems: State of the Art and Research Opportunities," Journal of Systems Architecture, Vol. 117, pp. 102137, 2021.
  8. F. Salewski, S. Kowalewski, "Hardware/Software Design Considerations for Automotive Embedded Systems," in IEEE Transactions on Industrial Informatics, Vol. 4, No. 3, pp. 156-163, 2008. https://doi.org/10.1109/TII.2008.2002919
  9. G. L. Gopu, K. V. Kavitha, J. Joy, "Service Oriented Architecture based Connectivity of Automotive ECUs," 2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT), pp. 1-4, 2018.
  10. C. Bradatsch, T. Ungerer, R. Zalman, A. Lajtkep, "Towards Runtime Testing in Automotive Embedded Systems," 2011 6th IEEE International Symposium on Industrial and Embedded Systems, pp. 55-58, 2011.
  11. S. H. Lee, D. K. Lee, P. Choi, D. Park, "Efficient Power Reduction Technique of LiDAR Sensor for Controlling Detection Accuracy Based on Vehicle Speed," IEMEK J. Embed. Sys. Appl., Vol. 15, No. 5, pp. 215-225, 2020 (in Korean). https://doi.org/10.14372/IEMEK.2020.15.5.215
  12. G. Xie, Y. Chen, Y. Liu, Y. Wei, R. Li, K. Li, "Resource Consumption Cost Minimization of Reliable Parallel Applications on Heterogeneous Embedded Systems," in IEEE Transactions on Industrial Informatics, Vol. 13, No. 4, pp. 1629-1640, 2017.
  13. G. N. Khan, J. Levman, J. Alirezaie, "Hardware-Software Co-Synthesis of Heterogeneous Embedded Computer Systems," 2006 Canadian Conference on Electrical and Computer Engineering, pp. 1304-1307, 2006.
  14. Y. H. Fan, J. O. Wu, S. F. Wang, "Software Synthesis of Middleware for Heterogeneous Embedded Systems," 2012 2nd International Conference on Consumer Electronics, Communications and Networks (CECNet), pp. 2084-2087, 2012.
  15. P. Gai, M. Violante, "Automotive Embedded Software Architecture in the Multi-core age," 2016 21th IEEE European Test Symposium (ETS), pp. 1-8, 2016.
  16. E. Diaz, E. Mezzetti, L. Kosmidis, J. Abella, F. J. Cazorla, "Modelling Multicore Contention on the AURIXTM TC27x," 2018 55th ACM/ESDA/IEEE Design Automation Conference (DAC), pp. 1-6, 2018.
  17. G. Xie, Y. Chen, Y. Liu, Y. Wei, R. Li, K. Li, "Resource Consumption Cost Minimization of Reliable Parallel Applications on Heterogeneous Embedded Systems," in IEEE Transactions on Industrial Informatics, Vol. 13, No. 4, pp. 1629-1640, 2017.
  18. https://www.infineon.com/cms/en/product/microcontroller/32-bit-tricore-microcontroller/32-bit-tricore-aurix-tc2xx/aurix-family-tc27xt/
  19. https://www.hitex.com/microcontroller-support/aurix/shieldbuddy-tc275