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 …

Deep neural network (DNN) inference on intermittently powered battery-less devices has the
potential to unlock new possibilities for sustainable and intelligent edge applications …

Edge servers have recently become very popular for performing localized analytics,
especially on video, as they reduce data traffic and protect privacy. However, due to their …

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

Autonomous Things (AuT) refers to a collection of self-sufficient tiny devices capable of
performing intelligent computations. Looking ahead, AuT promises to enable ubiquitous …

IoT devices are increasingly being implemented with neural network models to enable smart
applications. Energy harvesting (EH) technology that harvests energy from ambient …

Battery-less devices offer potential solutions for maintaining sustainable Internet of Things
(IoT) networks. However, limited energy harvesting capacity can lead to power failures …

The rapid and widespread deployment of Internet of Things (IoT) sensors is limited by issues
related to maintenance costs and safe battery disposal. While battery-less systems …

Energy-harvesting intelligent IoT devices are being attracted to provide sustainable
development and overcome unstable communication. However, executing multiple latency …

Powerful machine learning algorithms have been increasingly designed to achieve better
accuracy, which however require a great amount of data and computing power relying on …