Adaptable Realization of Industrial Analytics Functions on Edge-Devices using Reconfigurable Architectures

C.P. Gatica, M. Platzner, in: Machine Learning for Cyber Physical Systems (ML4CPS 2017), Berlin, Heidelberg, 2020.

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Gatica CP, Platzner M. Adaptable Realization of Industrial Analytics Functions on Edge-Devices using Reconfigurable Architectures. In: Machine Learning for Cyber Physical Systems (ML4CPS 2017). Berlin, Heidelberg; 2020. doi:10.1007/978-3-662-59084-3_9
Gatica, C. P., & Platzner, M. (2020). Adaptable Realization of Industrial Analytics Functions on Edge-Devices using Reconfigurable Architectures. In Machine Learning for Cyber Physical Systems (ML4CPS 2017). Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-59084-3_9
@inproceedings{Gatica_Platzner_2020, place={Berlin, Heidelberg}, title={Adaptable Realization of Industrial Analytics Functions on Edge-Devices using Reconfigurable Architectures}, DOI={10.1007/978-3-662-59084-3_9}, booktitle={Machine Learning for Cyber Physical Systems (ML4CPS 2017)}, author={Gatica, Carlos Paiz and Platzner, Marco}, year={2020} }
Gatica, Carlos Paiz, and Marco Platzner. “Adaptable Realization of Industrial Analytics Functions on Edge-Devices Using Reconfigurable Architectures.” In Machine Learning for Cyber Physical Systems (ML4CPS 2017). Berlin, Heidelberg, 2020. https://doi.org/10.1007/978-3-662-59084-3_9.
C. P. Gatica and M. Platzner, “Adaptable Realization of Industrial Analytics Functions on Edge-Devices using Reconfigurable Architectures,” in Machine Learning for Cyber Physical Systems (ML4CPS 2017), 2020.
Gatica, Carlos Paiz, and Marco Platzner. “Adaptable Realization of Industrial Analytics Functions on Edge-Devices Using Reconfigurable Architectures.” Machine Learning for Cyber Physical Systems (ML4CPS 2017), 2020, doi:10.1007/978-3-662-59084-3_9.

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