HiMLEdge – Energy-Aware Optimization for Hierarchical Machine Learning

Abstract

Smart sensor systems are a key factor to ensure sustainable compute by enabling machine learning algorithms to be executed at the data source. This is particularly helpful when working with moving parts or in remote areas, where no tethered deployment is possible. However, including computations directly at the measurement device places an increased load on the power budget. Therefore, we introduce the Hierarchical Machine Learning framework “HiMLEdge” which enables highly specialized models that are tuned using an energy-aware multi-criteria optimization. We evaluate our framework with prognostic health management in a three-part feasibility study: First, we apply an exhaustive search to find hierarchical taxonomies, which we benchmark against hand-tuned flat classifiers. This test shows a decrease in power consumption of up to 47.63% for the hierarchical approach. Second, the search strategy is improved with Reinforcement Learning. As a novel contribution, we include real measurements in the reward function, instead of using a surrogate metric. This inclusion leads to a different optimal policy in comparison to the literature, which shows the error that may be introduced by an approximation. Third, we conduct tests on the system level, including communication and system-off power draw. In this scenario, the optimized hierarchical model can perform four times as many readings per hour as a flat classifier while achieving the same five years of battery life with similar accuracy. In turn, this also means that the battery life can be increased by the same amount if the readings per hour are kept constant.