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  4. Publikationen School of Engineering
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dc.contributor.authorZingg, Raphael-
dc.contributor.authorRosenthal, Matthias-
dc.date.accessioned2020-05-25T09:05:20Z-
dc.date.available2020-05-25T09:05:20Z-
dc.date.issued2020-02-26-
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/20055-
dc.description.abstractUsing artificial intelligence algorithms such as neural networks on microcontrollers offers several possibilities but reveals challenges: limited memory, low computing power and no operating system. In addition, an efficient workflow to port neural networks algorithms to microcontrollers is required. Currently, several frameworks that can be used to port neural networks to microcontrollers are available. This paper evaluates and compares four of them: ”TensorFlow Lite for Microcontrollers” from Google, ”Neural Network on Microcontroller” from Jianjia Ma with the CMSIS-NN backend from ARM, X-CUBE-AI from STMicroelectronics and the e-Ai Solution from Renesas. The frameworks differ considerably in terms of workflow, features and performance. Depending on the application, one framework is better suited than another. Neural networks that are ported to microcontrollers with those frameworks are static. This means that once they are integrated into the firmware they can no longer be adapted. In the context of this work, possibilities of unsupervised domain adaptation learning on microcontrollers was investigated and is discussed.de_CH
dc.language.isoende_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectArtificial intelligencede_CH
dc.subjectMicrocontrollerde_CH
dc.subjectNeural networkde_CH
dc.subjectEmbedded systemde_CH
dc.subjectEmbedded unsupervised domain adaptationde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.titleArtificial intelligence on microcontrollersde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitute of Embedded Systems (InES)de_CH
zhaw.conference.detailsEmbedded World Conference 2020, Nürnberg, 25.-27. Februar 2020de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.publication.reviewPeer review (Abstract)de_CH
zhaw.webfeedDigitale Signalverarbeitungde_CH
zhaw.webfeedIndustrie 4.0de_CH
zhaw.webfeedSensorikde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
Appears in collections:Publikationen School of Engineering

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Zingg, R., & Rosenthal, M. (2020, February 26). Artificial intelligence on microcontrollers. Embedded World Conference 2020, Nürnberg, 25.-27. Februar 2020.
Zingg, R. and Rosenthal, M. (2020) ‘Artificial intelligence on microcontrollers’, in Embedded World Conference 2020, Nürnberg, 25.-27. Februar 2020.
R. Zingg and M. Rosenthal, “Artificial intelligence on microcontrollers,” in Embedded World Conference 2020, Nürnberg, 25.-27. Februar 2020, Feb. 2020.
ZINGG, Raphael und Matthias ROSENTHAL, 2020. Artificial intelligence on microcontrollers. In: Embedded World Conference 2020, Nürnberg, 25.-27. Februar 2020. Conference paper. 26 Februar 2020
Zingg, Raphael, and Matthias Rosenthal. 2020. “Artificial Intelligence on Microcontrollers.” Conference paper. In Embedded World Conference 2020, Nürnberg, 25.-27. Februar 2020.
Zingg, Raphael, and Matthias Rosenthal. “Artificial Intelligence on Microcontrollers.” Embedded World Conference 2020, Nürnberg, 25.-27. Februar 2020, 2020.


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