EmbML Tool: Supporting the use of Supervised Learning Algorithms in LowCost Embedded Systems Machine Learning (ML) is becoming a ubiquitous technology employed in many realworld 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 highlyefficient classifiers suitable to execute in unresourceful hardware. However, this scenario conflicts to the stateofpractice of ML, in which classifiers are frequently implemented in highlevel interpreted languages, make unrestricted use of floatingpoint operations and assume plenty of resources. In this paper, we present a software tool named EmbML that implements a pipeline to develop classifiers for lowpowered 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.

Energy Efficient Implementation of Machine Learning Algorithms on Hardware Platforms Machine and deep learning algorithms are currently employed for many applications such as computer vision, speech recognition and portablewearable electronics. Regrettably, machine learning algorithms have high complexity adding more challenges for the implementation of such algorithms on embedded hardware platforms. This paper aims to present an overview about state of the art techniques enabling efficient implementation of Machine and Deep learning (MLDL) algorithms aiming to improve the energy efficiency. An assessment of the algorithms suitable for embedded implementation is provided, presenting some hardware platforms supporting artificial intelligent systems. On the other hand, we have exploited the choice of implementing MLDL algorithm on embedded hardware platforms.