| Full text | |
| Author(s): |
da Silva, Lucas Tsutsui
;
Souza, Vinicius M. A.
;
Batista, Gustavo E. A. P. A.
;
IEEE
Total Authors: 4
|
| Document type: | Journal article |
| Source: | 2019 IEEE 31ST INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI 2019); v. N/A, p. 5-pg., 2019-01-01. |
| Abstract | |
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) | |
| FAPESP's process: | 16/04986-6 - Intelligent traps and sensors: an innovative approach to control insect pests and disease vectors |
| Grantee: | Gustavo Enrique de Almeida Prado Alves Batista |
| Support Opportunities: | Research Grants - eScience and Data Science Program - Regular Program Grants |
| FAPESP's process: | 18/05859-3 - Intelligent traps and sensors: an innovative approach to control insect pests and disease vectors |
| Grantee: | Vinícius Mourão Alves de Souza |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |