| Texto completo | |
| Autor(es): |
da Silva, Lucas Tsutsui
;
Souza, Vinicius M. A.
;
Batista, Gustavo E. A. P. A.
;
IEEE
Número total de Autores: 4
|
| Tipo de documento: | Artigo Científico |
| Fonte: | 2019 IEEE 31ST INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI 2019); v. N/A, p. 5-pg., 2019-01-01. |
| Resumo | |
Machine Learning (ML) is becoming a ubiquitous technology employed in many real-world applications. In some applications, sensors measure the environment while ML algorithms are responsible for interpreting the data. These systems often face three main restrictions: power consumption, cost, and lack of infrastructure. Therefore, we need highly-efficient classifiers suitable to execute in unresourceful hardware. However, this scenario conflicts to the state-of-practice of ML, in which classifiers are frequently implemented in high-level interpreted languages, make unrestricted use of floating-point operations and assume plenty of resources. In this paper, we present a software tool named EmbML that implements a pipeline to develop classifiers for low-powered embedded systems. It starts with learning a classifier using popular software packages or libraries. Then, EmbML converts the classifier into a carefully crafted C++ code with support for embedded hardware. Our experimental evaluation shows that EmbML classifiers present competitive results in terms of accuracy, time and memory cost. (AU) | |
| Processo FAPESP: | 16/04986-6 - Armadilhas e sensores inteligentes: uma abordagem inovadora para controle de insetos peste e vetores de doenças |
| Beneficiário: | Gustavo Enrique de Almeida Prado Alves Batista |
| Modalidade de apoio: | Auxílio à Pesquisa - Programa eScience e Data Science - Regular |
| Processo FAPESP: | 18/05859-3 - Armadilhas e sensores inteligentes: uma abordagem inovadora para controle de insetos peste e vetores de doenças |
| Beneficiário: | Vinícius Mourão Alves de Souza |
| Modalidade de apoio: | Bolsas no Brasil - Pós-Doutorado |