Name
Title | ||
|---|---|---|
Embml Tool: Supporting The Use Of Supervised Learning Algorithms In Low-Cost Embedded Systems |
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICTAI.2019.00238 | 2019 IEEE 31ST INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI 2019) |
Keywords | Field | DocType |
machine learning, embedded systems, embedded classifier, WEKA, scikit-learn | Software tool,Embedded hardware,Ubiquitous technology,Computer science,Software,Artificial intelligence,Supervised training,Interpreted language,Classifier (linguistics),Machine learning,Embedded system,Power consumption | Conference |
ISSN | Citations | PageRank |
1082-3409 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lucas Tsutsui da Silva | 1 | 0 | 0.34 |
| Vinícius M. A. de Souza | 2 | 33 | 6.14 |
| Gustavo E. Batista | 3 | 1928 | 92.83 |