TinyML for Small Microcontrollers
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 c...
Guardado en:
| Autores principales: | , , , |
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| Formato: | Objeto de conferencia |
| Lenguaje: | Inglés |
| Publicado: |
2022
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| Materias: | |
| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/140652 |
| Aporte de: |
| Sumario: | 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. |
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