Goal

This workshop aims to bring together people from many different areas in data mining and machine learning that have a common interest in energy efficiency, energy aware computing, hardware accelerators, and/or sustainability. Nowadays we can observe how machine learning algorithms are responsible for a significant amount of energy consumption in data centers. Examples are Facebook and Google data centers running tasks such as speech and image recognition. Today’s data centers consume 1.8% of the total energy consumption in the United States, based on a 2016 report from the Lawrence Berkeley National Laboratory. Data mining and machine learning researchers have a chance to make an impact in today’s society, by building not only accurate and scalable applications, but also applications and algorithms that are both energy efficient and energy aware. Researchers in this field are starting to realize that tasks such as deep learning consume a notable amount of energy, and that it should not be ignored. This is why there is a trend in developing specific hardware for running deep neural networks at a reduced energy consumption1 2 .

Our ultimate goal is to promote green computing in machine learning and data mining, so that researchers understand the impact of their computations. We are interested in papers that deal with the application of data mining and knowledge dis- covery methods for solving fundamental and/or applied problems in energy-efficiency and energy-aware computing. Topics of interest include, but are not limited to: energy-efficient data mining, energy-efficient hardware, domain-specific architec- tures for data mining and machine learning, green data mining, green machine learning, energy-efficient big data platforms, energy-efficient deep learning, energy- efficient data centers, Internet of Things (IoT), large-scale computing, data profiling, scalable algorithms, energy-efficient stream mining, distributed stream mining.