Worst-Case Energy Consumption: A New Challenge for Battery-Powered Critical Devices

[EN] The number of (edge) devices connected to the IoT is on the rise, reaching hundreds of billions in the next years. Many devices will implement some type of critical functionality, for instance in the medical market this includes infusion pumps and implantable defibrillators. Energy awareness is mandatory in the design of IoT devices given their huge impact on worldwide energy consumption and the fact that many of them are battery powered. Critical IoT devices further require addressing new energy-related challenges. On the one hand, factoring in the impact of energy-solutions on device's performance, providing evidence of adherence to domain-specific safety standards. On the other hand, deriving safe worst-case energy consumption (WCEC) estimates is fundamental to ensure the system can continuously operate under a pre-established set of power/energy caps, safely delivering its critical functionality. In this line, we analyze for the first time the impact that different hardware physical parameters have on both model-based and measurement-based WCEC modeling, for which we also show the main challenges they face compared to chip manufacturers' current practice for energy modeling and validation. Under the set of constraints that emanate from how certain physical parameters can be actually modeled, we show that measurement-based WCEC is a promising way forward for WCEC estimation.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness (MINECO) under grant TIN2015-65316-P and the HiPEAC Network of Excellence. Jaume Abella has been partially supported by the MINECO under Ramon y Cajal postdoctoral fellowship number RYC-2013-14717. Carles Hernndez is jointly funded by the MINECO and FEDER funds through grant TIN2014-60404-JIN.Trilla, D.; Hernández Luz, C.; Abella, J.; Cazorla, FJ. (2021). Worst-Case Energy Consumption: A New Challenge for Battery-Powered Critical Devices. IEEE Transactions on Sustainable Computing. 6(3):522-530. https://doi.org/10.1109/TSUSC.2019.29431425225306