EmbML Tool: supporting the use of supervised learning algorithms in low-cost embedded systems

Autor(es)

da Silva, Lucas Tsutsui ; Souza, Vinicius M. A. ; Batista, Gustavo E. A. P. A. ; IEEE

• 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