With the advent of ultra-low-power embedded processors, energy harvesting devices
(EHDs) are becoming exceedingly prevalent. These devices are highly portable, self …

The increasing paradigm shift towards i ntermittent computing has made it possible to
intermittently execute d eep neural network (DNN) inference on edge devices powered by …

Internet-of-Things (IoT) devices are becoming both intelligent and green. On the one hand,
Deep Neural Network (DNN) compression techniques make it possible to run deep learning …

In healthcare, anomaly detection has emerged as a central application. This study presents
an ultra-low power processor tailored for wearable devices dedicated to anomaly detection …

Abstract Battery-powered, ultra-low-power embedded devices are often limited by the size
and maintenance costs of batteries, giving rise to battery-less devices and the emergence of …

Intermittent deep neural network (DNN) inference is a promising technique to enable
intelligent applications on tiny devices powered by ambient energy sources. Nonetheless …

IoT systems can operate efficiently in scenarios with limited or sporadic power availability by
utilizing intermittent power sources, such as energy harvesting. Typical implementations are …

Battery-powered IoT devices face challenges like cost, maintenance, and environmental
sustainability, prompting the emergence of batteryless energy-harvesting systems that …

Computer systems that operate on volatile energy sources typically rely on intermittent
computing approaches, which involve checkpointing the system's state and persisting a …

Firmware update is an essential service on Internet-of-Things (IoT) devices to fix
vulnerabilities and add new functionalities. Firmware update is energy-consuming since it …