Energy Efficient Deep-Edge Computing through Hardware Machine Learning

It is currently challenging to accomplish high data processing with low latency, because large-scale, persistent, high-bandwidth connection is often required to gather huge volumes of data from platforms and sensors operating at the edge to allow processing in the cloud. Internet-of-Things (IoT) devices deployment in remote locations for environmental monitoring with machine learning (ML) techniques, Internetof-Medical Things, IoT-based precision agriculture, all can operate with limited battery, while robustness and reliability is required. Machine learning algorithms employed in IoT devices improves efficiency as well as decision-making with real-time situational awareness and the capacity to react swiftly and accurately to impending actions or threats. This work presents a methodology to develop AI-based applications for Internet-ofThings environments by using the machine learning core inside the STM Mems Sensor LSM6DSOX and the toolchain Unico, Unicleo and WEKA. In particular, we demonstrate an efficient implementation that combines the target IoT platform's compilation toolchain and platform-specific resource limitations into the model selection process, to leverage embedded hardware machine learning for automated monitoring of the detection of a falling object.