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graduation-thesis

https://github.com/taffarel55/graduation-thesis

Science Score: 26.0%

This score indicates how likely this project is to be science-related based on various indicators:

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    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
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    .zenodo.json file
    Found .zenodo.json file
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    DOI references
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    Scientific vocabulary similarity
    Low similarity (4.5%) to scientific vocabulary
Last synced: 6 months ago · JSON representation

Repository

Basic Info
  • Host: GitHub
  • Owner: taffarel55
  • Language: Jupyter Notebook
  • Default Branch: main
  • Size: 101 MB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 1
Created over 2 years ago · Last pushed about 2 years ago
Metadata Files
Readme Citation

README.md

Graduation Thesis

Maurcio Taffarel

Resumo

A tcnica TinyML refere-se ao conjunto de abordagens que viabilizam a implementao de algoritmos de aprendizado de mquina em dispositivos com recursos computacionais e capacidade de memria restritos, como sistemas embarcados. Este trabalho abordou duas maneiras de implementar tais tcnicas: otimizao e compactao de modelos, explorando diferentes tecnologias. Alm disso, foram apresentados detalhes especficos relacionados a essa abordagem do TinyML no processo de desenvolvimento, com nfase na portabilidade e escalabilidade. A avaliao da soluo proposta permitir analisar o impacto e a eficcia do uso do TinyML na implementao de sistemas de aprendizado de mquina em microcontroladores com recursos limitados.

Palavras-chave: - TinyML - Inteligncia Artificial - Sistemas Embarcados - Portabilidade

Abstract

The TinyML technique refers to the set of approaches that enable the implementation of machine learning algorithms in devices with limited computational resources and memory capacity, such as embedded systems. This work addressed two ways to implement such techniques: model optimization and compression, exploring different technologies. In addition, specific details related to this TinyML approach in the development process were presented, with an emphasis on portability and scalability. The evaluation of the proposed solution will allow to analyze the impact and effectiveness of the use of TinyML in the implementation of machine learning systems in microcontrollers with limited resources.

Keywords: - TinyML - Artificial Intelligence - Embedded Systems - Portability

Owner

  • Name: Mauricio Taffarel
  • Login: taffarel55
  • Kind: user
  • Location: São José dos Campos, SP
  • Company: Embraer

IoT Developer

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Dependencies

TensorFlow Lite for uC/1. Creating model/requirements.txt pypi
  • matplotlib ==3.7.1
  • tensorflow ==2.12
TensorFlow Lite for uC/2. Build on ESP32/requirements.txt pypi
  • matplotlib ==3.7.1
  • numpy ==1.24.3