EVE: Environmental Adaptive Neural Network Models for Low-power Energy Harvesting System

However, when IoT devices are increasingly being implemented with neural network DNN models come to on-board, there is a grand challenge to accommodate models to enable smart applications. Energy harvesting (EH) the giant models to tiny IoT devices with limited memory technology that harvests energy from ambient environment is a and computing resources [3, 11-13, 20, 22]. Particularly, first, embedded promising alternative to batteries for powering those devices due IoT devices have limited computational units and low CPU to the low maintenance cost and wide availability of the energy frequency (e.g., 1-16MHZ). Since DNNs are computationally expensive, sources. However, the power provided by the energy harvester is DNN algorithm takes long on-board execution time. Second, low and has an intrinsic drawback of instability since it varies with embedded IoT devices are equipped with small memory (e.g., hundreds the ambient environment. This paper proposes EVE, an automated of KBs) which can not even afford tiny DNN models (e.g., machine learning (autoML) co-exploration framework to search Tens of MBs). Third, these battery-powered devices naturally have for desired multi-models with shared weights for energy harvesting a limited standby time.