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Energy-demand estimation of embedded devices using deep artificial neural networks

Publication by Timo Hönig, Benedict Herzog, Wolfgang Schröder-Preikschat
Related to the Energy-, Latency- And Resilience-aware Networking (e.LARN) project
Published in SAC '19: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, 2019


The need for high performance in embedded devices grows at a breathtaking pace. Embedded processors that satisfy the hunger for superlative processing power share a common issue: the increasing performance leads to growing energy demands during operation. As energy remains a limited resource to embedded devices, it is critical to optimise software components for low power. Low-power software needs energy models which, however, are increasingly difficult to create as to the complexity of today's devices. In this paper we present a black-box approach to construct precise energy models for complex hardware devices. We apply machine-learning techniques in combination with fully automatic energy measurements and evaluate our approach with an ARM Cortex platform. We show that our system estimates the energy demand of program code with a mean percentage error of 1.8% compared to the results of energy measurements.