Power Profiler: Monitoring Energy Consumption of ML Algorithms on Android Mobile Devices

Energy efficiency is a critical concern for machine learning (ML) algorithms deployed in data centers. Recently, many works in the literature have focused on running ML algorithms on energy-efficient and constrained hardware, such as mobile phones, to reduce the energy footprint of training ML models. This paper introduces the Power Profiler, an open-source monitoring platform that provides valuable insights into the energy consumption of ML algorithms on Android mobile devices. By capturing key performance indicators (KPIs) such as voltage, current, and CPU usage, the Power Profiler enables real-time monitoring of their energy usage. It eliminates the need for custom hardware installations and facilitates the development of energy-efficient ML models. The Power Profiler can empower researchers to understand and optimize the energy consumption patterns of ML algorithms, facilitating the creation of sustainable ML models for energy-efficient mobile deployments.