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

Battery-free and intermittently powered devices offer long lifetimes and enable deployment
in new applications and environments. Unfortunately, developing sophisticated inference …

This paper introduces power failure immunity (PFI), an essential program execution property
for energy harvesting systems to achieve efficient intermittent computation. PFI ensures …

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 …

Backing up the intermediate results of hardware-accelerated deep inference is crucial to
ensure the progress of execution on batteryless computing platforms. However, hardware …

Energy harvesting (EH) IoT devices that operate intermittently without batteries, coupled with
advances in deep neural networks (DNNs), have opened up new opportunities for en-abling …

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 …

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 …