TinyML for Small Microcontrollers
- Autores
- Estrebou, César Armando; Saavedra, Marcos David; Adra, Federico; Fleming, Martín
- Año de publicación
- 2022
- Idioma
- inglés
- Tipo de recurso
- documento de conferencia
- Estado
- versión publicada
- Descripción
- This paper describes the progress made in the context of a research and development project on machine learning techniques and algorithms applied to small microcontrollers. The beginning of the development of EmbedIA, a machine learning framework for microcontrollers, is presented. The experiments carried out comparing the proposed framework with other similar frameworks such as Tensorflow Lite Micro, μTensor and EloquentTinyML show an important advantage with respect to memory and inference time required by small microcontrollers.
Instituto de Investigación en Informática - Materia
-
Ciencias Informáticas
Machine Learning
Embedded Systems
Microcontrollers
IoT
Convolutional Neural Networks
TinyML - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by-nc-sa/4.0/
- Repositorio
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- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/140652
Ver los metadatos del registro completo
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TinyML for Small MicrocontrollersEstrebou, César ArmandoSaavedra, Marcos DavidAdra, FedericoFleming, MartínCiencias InformáticasMachine LearningEmbedded SystemsMicrocontrollersIoTConvolutional Neural NetworksTinyMLThis paper describes the progress made in the context of a research and development project on machine learning techniques and algorithms applied to small microcontrollers. The beginning of the development of EmbedIA, a machine learning framework for microcontrollers, is presented. The experiments carried out comparing the proposed framework with other similar frameworks such as Tensorflow Lite Micro, μTensor and EloquentTinyML show an important advantage with respect to memory and inference time required by small microcontrollers.Instituto de Investigación en Informática2022-07info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf42-46http://sedici.unlp.edu.ar/handle/10915/140652enginfo:eu-repo/semantics/altIdentifier/isbn/978-950-34-2126-0info:eu-repo/semantics/reference/hdl/10915/139373info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-10-22T17:16:37Zoai:sedici.unlp.edu.ar:10915/140652Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-22 17:16:37.712SEDICI (UNLP) - Universidad Nacional de La Platafalse |
| dc.title.none.fl_str_mv |
TinyML for Small Microcontrollers |
| title |
TinyML for Small Microcontrollers |
| spellingShingle |
TinyML for Small Microcontrollers Estrebou, César Armando Ciencias Informáticas Machine Learning Embedded Systems Microcontrollers IoT Convolutional Neural Networks TinyML |
| title_short |
TinyML for Small Microcontrollers |
| title_full |
TinyML for Small Microcontrollers |
| title_fullStr |
TinyML for Small Microcontrollers |
| title_full_unstemmed |
TinyML for Small Microcontrollers |
| title_sort |
TinyML for Small Microcontrollers |
| dc.creator.none.fl_str_mv |
Estrebou, César Armando Saavedra, Marcos David Adra, Federico Fleming, Martín |
| author |
Estrebou, César Armando |
| author_facet |
Estrebou, César Armando Saavedra, Marcos David Adra, Federico Fleming, Martín |
| author_role |
author |
| author2 |
Saavedra, Marcos David Adra, Federico Fleming, Martín |
| author2_role |
author author author |
| dc.subject.none.fl_str_mv |
Ciencias Informáticas Machine Learning Embedded Systems Microcontrollers IoT Convolutional Neural Networks TinyML |
| topic |
Ciencias Informáticas Machine Learning Embedded Systems Microcontrollers IoT Convolutional Neural Networks TinyML |
| dc.description.none.fl_txt_mv |
This paper describes the progress made in the context of a research and development project on machine learning techniques and algorithms applied to small microcontrollers. The beginning of the development of EmbedIA, a machine learning framework for microcontrollers, is presented. The experiments carried out comparing the proposed framework with other similar frameworks such as Tensorflow Lite Micro, μTensor and EloquentTinyML show an important advantage with respect to memory and inference time required by small microcontrollers. Instituto de Investigación en Informática |
| description |
This paper describes the progress made in the context of a research and development project on machine learning techniques and algorithms applied to small microcontrollers. The beginning of the development of EmbedIA, a machine learning framework for microcontrollers, is presented. The experiments carried out comparing the proposed framework with other similar frameworks such as Tensorflow Lite Micro, μTensor and EloquentTinyML show an important advantage with respect to memory and inference time required by small microcontrollers. |
| publishDate |
2022 |
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2022-07 |
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info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/publishedVersion Objeto de conferencia http://purl.org/coar/resource_type/c_5794 info:ar-repo/semantics/documentoDeConferencia |
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eng |
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eng |
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