Inteligência artificial embarcada
experimentos com utilização de KNN em FPGAs
DOI:
https://doi.org/10.47734/rce.v5iFC.2687Palavras-chave:
aprendizado de máquina, inteligência artificial, sistemas embarcados, FPGAResumo
Este artigo explora a viabilidade de implementar o algoritmo K-Vizinhos Mais Próximos (KNN) em circuitos integrados do tipo FPGA. A metodologia adotada compreendeu o desenvolvimento inicial do algoritmo em linguagem Python e C, seguido pela implementação em linguagem VHDL para hardware FPGA. Foram realizados experimentos utilizando os conjuntos de dados Iris, Wine e Digitos, com variações nos parâmetros para avaliar o desempenho e a precisão. A implementação do KNN em FPGA, embora tenha demonstrado uma acurácia ligeiramente inferior à das versões em software, confirmou a viabilidade e a eficiência desta abordagem. A diferença na acurácia é atribuída à utilização de números de ponto fixo, que oferecem precisão limitada em comparação aos números de ponto flutuante usados em software. A análise do uso dos recursos dos sistemas sintetizados mostrou que a implementação em FPGA é viável e eficiente, sugerindo que dispositivos de hardware programável são uma opção promissora para o uso de algoritmos de aprendizado de máquina. Esses resultados indicam um caminho claro para futuras otimizações e a potencial implementação de algoritmos de aprendizado de máquina mais complexos em FPGAs, expandindo o escopo de aplicações avançadas.
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Copyright (c) 2025 Luís Otávio Amorim, Dr. Ricardo Pires, Gustavo Lucente
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