Abstract
Cyber-Physical Systems (CPS) are interconnected devices, reactive and dynamic to sensed external and internal triggers. The H2020 CERBERO EU Project is developing a design environment composed by modelling, deployment and verification tools for adaptive CPS. This paper focuses on its efficient support for run-time self-adaptivity.
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Notes
- 1.
Cross-layer modEl-based fRamework for multi-oBjective dEsign of Reconfigurable systems in unceRtain hybRid envirOnments - (http://www.cerbero-h2020.eu/).
- 2.
Experiments available at: http://youtu.be/a9WIucWfjkU.
References
CERBERO Deliverable D3.4 - CERBERO Modelling of KPI. https://www.cerbero-h2020.eu/wp-content/uploads/2018/12/D3.4.pdf
CERBERO Deliverable D6.8 - Planetary Exploration Demonstrator. https://www.cerbero-h2020.eu/wp-content/uploads/2018/12/D6.8.pdf
Performance API (2019). http://icl.utk.edu/papi/
Bosse, T., et al.: Developing ePartners for human-robot teams in space based on ontologies and formal abstraction hierarchies. J. Agent-Orient. Softw. Eng. 5(4), 366–398 (2017)
Brun, Y., et al.: Engineering self-adaptive systems through feedback loops. In: Cheng, B.H.C., de Lemos, R., Giese, H., Inverardi, P., Magee, J. (eds.) Software Engineering for Self-Adaptive Systems. LNCS, vol. 5525, pp. 48–70. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-02161-9_3
Desnos, K., et al.: PiMM: parameterized and interfaced dataflow meta-model for MPSoCs runtime reconfiguration. In: SAMOS (2013)
Fanni, T., et al.: Multi-grain reconfiguration for advanced adaptivity in cyber-physical systems. In: ReConFig 2018, December 2018
Hartenstein, R.: Coarse grain reconfigurable architecture (embedded tutorial). In: Conference of the Asia and South Pacific Design Automation (2001)
Heulot, J., et al.: SPIDER: a synchronous parameterized and interfaced dataflow-based RTOS for multicore DSPs. In: EDERC (2014)
Kim, K., Kumar, P.R.: Cyber-physical systems: a perspective at the centennial. Proc. IEEE 100(Special Centennial Issue), 1287–1308 (2012)
Leeuwen, C.J.V., et al.: Model-based architecture optimization for self-adaptive networked signal processing systems. In: SASO (2014)
Lombardo, M., et al.: Power management techniques in an FPGA-based WSN node for high performance applications. In: ReCoSoC (2012)
Macías-Escrivá, F.D., et al.: Self-adaptive systems: a survey of current approaches, research challenges and applications. Expert Syst. Appl. 40(18), 7267–7279 (2013)
Madroñal, D., Fanni, T.: Run-time performance monitoring of hardware accelerators: POSTER. In: CF (2019)
Madroñal, D., et al.: Automatic instrumentation of dataflow applications using PAPI. In: CF (2018)
Masin, M., et al.: Cross-layer design of reconfigurable cyber-physical systems. In: DATE, March 2017
Narizzano, M., Pulina, L., Tacchella, A., Vuotto, S.: Consistency of property specification patterns with boolean and constrained numerical signals. In: Dutle, A., Muñoz, C., Narkawicz, A. (eds.) NFM 2018. LNCS, vol. 10811, pp. 383–398. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-77935-5_26
Palumbo, F., et al.: Power-awarness in coarse-grained reconfigurable multi-functional architectures: a dataflow based strategy. J. Signal Process. Syst. 87(1), 81–106 (2017). https://doi.org/10.1007/s11265-016-1106-9
Palumbo, F., et al.: CERBERO: cross-layer model-based framework for multi-objective design of reconfigurable systems in uncertain hybrid environments. In: CF (2019)
Pelcat, M., et al.: PREESM: a dataflow-based rapid prototyping framework for simplifying multicore DSP programming. In: EDERC (2014)
Pelcat, M., et al.: Reproducible evaluation of system efficiency with a model of architecture: from theory to practice. IEEE Trans. Comput. Aided Design Integr. Circ. Syst. 37, 2050–2063 (2017)
Ren, R., et al.: Energy estimation models for video decoders: reconfigurable video coding-CAL case-study. IET Comput. Digit. Tech. 9(1), 3–15 (2014)
Rodríguez, A., et al.: FPGA-based high-performance embedded systems for adaptive edge computing in cyber-physical systems: the ARTICo3 framework. Sensors 18(6), 1877 (2018). https://doi.org/10.3390/s18061877
Salehie, M., Tahvildari, L.: Towards a goal-driven approach to action selection in self-adaptive software. Softw. Pract. Exper. 42(2), 211–233 (2012)
Suriano, L., et al.: A unified hardware/software monitoring method for reconfigurable computing architectures using PAPI. In: ReCoSoC (2018)
Zadorojniy, A., et al.: Algorithms for finding maximum diversity of design variables in multi-objective optimization. In: CSER (2012)
Acknowledgments
This work has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 732105. The authors would like to thank the Spanish Ministry of Education, Culture and Sport for its support under the FPU grant program.
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Palumbo, F. et al. (2019). Hardware/Software Self-adaptation in CPS: The CERBERO Project Approach. In: Pnevmatikatos, D., Pelcat, M., Jung, M. (eds) Embedded Computer Systems: Architectures, Modeling, and Simulation. SAMOS 2019. Lecture Notes in Computer Science(), vol 11733. Springer, Cham. https://doi.org/10.1007/978-3-030-27562-4_30
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